NBA: Basketball-Reference.com: W classification:: Wins2
bdanalytics
Date: (Wed) Jun 17, 2015
Introduction:
Data: Source: Training: https://courses.edx.org/asset-v1:MITx+15.071x_2a+2T2015+type@asset+block/NBA_train.csv
New: https://courses.edx.org/asset-v1:MITx+15.071x_2a+2T2015+type@asset+block/NBA_test.csv
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Regression results: First run:
First run: All.X.lm: OOB_RMSE=2.8780; new_RMSE=2.8779; oppPTS=100.00; Playoffs=24.27; PTS=11.73
-Playoffs features: All.X.lm: OOB_RMSE=3.1678; new_RMSE=3.1677; oppPTS=100.00; PTS=7.42; BLK=4.87; .rnorm=0.00 PTS.only.lm: OOB_RMSE=3.0993; new_RMSE=3.0993; oppPTS=100.00; PTS=0.00 PTS.interact.lm: OOB_RMSE=3.0888; new_RMSE=3.0888; oppPTS=100.00; PTS=81.56; oppPTS:PTS=0.00
-Playoffs +PTS.diff features: Interact.High.cor.Y.lm:
OOB_RMSE=3.0558; new_RMSE=3.0558; PTS.diff=100.00; DRB=27.25
Classification results: First run:
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glb_txt_vars
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Convert user-specified mutate code to config specs
- Add n-grams for glb_txt_vars
- fit.models chunk:
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- move model_type parameter to myfit_mdl before indep_vars_vctr (keep all model_* together)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- Skip trControl.method=“cv” for dummy classifier ?
- Add custom model to caret for a dummy (baseline) classifier (binomial & multinomial) that generates proba/outcomes which mimics the freq distribution of glb_rsp_var values; Right now glb_dmy_glm_mdl always generates most frequent outcome in training data
glm_dmy_mdl should use the same method as glm_sel_mdl until custom dummy classifer is implemented
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Replicate myfit_mdl_classification features in myfit_mdl_regression
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list=ls())
set.seed(12345)
options(stringsAsFactors=FALSE)
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mydsutils.R")## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
registerDoMC(4) # max(length(glb_txt_vars), glb_n_cv_folds) + 1
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
glb_trnng_url <- "https://courses.edx.org/asset-v1:MITx+15.071x_2a+2T2015+type@asset+block/NBA_train.csv"
glb_newdt_url <- "https://courses.edx.org/asset-v1:MITx+15.071x_2a+2T2015+type@asset+block/NBA_test.csv"
glb_out_pfx <- "Wins2_"
glb_save_envir <- FALSE # or TRUE
glb_is_separate_newent_dataset <- TRUE # or TRUE
glb_split_entity_newent_datasets <- TRUE # or FALSE
glb_split_newdata_method <- "sample" # "condition" or "sample" or "copy"
glb_split_newdata_condition <- NULL # or "is.na(<var>)"; "<var> <condition_operator> <value>"
glb_split_newdata_size_ratio <- 0.3 # > 0 & < 1
glb_split_sample.seed <- 123 # or any integer
glb_max_fitent_obs <- NULL # or any integer
glb_is_regression <- TRUE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE # or TRUE or FALSE
glb_rsp_var_raw <- "W"
# for classification, the response variable has to be a factor
glb_rsp_var <- glb_rsp_var_raw # or "<glb_rsp_var_raw>.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- NULL # or function(raw) {
# relevel(factor(ifelse(raw == 1, "Y", "N")), as.factor(c("Y", "N")), ref="N")
# #as.factor(paste0("B", raw))
# #as.factor(gsub(" ", "\\.", raw))
# }
# glb_map_rsp_raw_to_var(c(1, 1, 0, 0, 0))
glb_map_rsp_var_to_raw <- NULL # or function(var) {
# as.numeric(var) - 1
# #as.numeric(var)
# #gsub("\\.", " ", levels(var)[as.numeric(var)])
# #c(" <=50K", " >50K")[as.numeric(var)]
# #c(FALSE, TRUE)[as.numeric(var)]
# }
# glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(c(1, 1, 0, 0, 0)))
if ((glb_rsp_var != glb_rsp_var_raw) & is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
glb_rsp_var_out <- paste0(glb_rsp_var, ".predict.") # model_id is appended later
# List info gathered for various columns
# <col_name>: <description>; <notes>
# SeasonEnd: is the year the season ended.
# Team: Team ID
# Playoffs: 1 if team made it to the playoffs that year else 0
# W: # of regular season wins.
# PTS: # of points scored during the regular season.
# oppPTS: # of opponent points scored during the regular season.
# FG: # of successful field goals, including two and three pointers.
# FGA: # of FG attempts
# X2P: # of 2 point field goals made
# X2PA: # of 2 point field goals attmpted
# X3P, X3PA: # of 3 point field goals
# FT, FTA: # of free throws
# ORB, DRB: # of offensive and defensive rebounds.
# AST: # of assists.
# STL: # of steals.
# BLK: # of blocks.
# TOV: # of turnovers.
# If multiple vars are parts of id, consider concatenating them to create one id var
# If glb_id_var == NULL, ".rownames <- row.names()" is the default
glb_id_var <- NULL # or c("<var1>")
glb_category_vars <- NULL # or c("<var1>", "<var2>")
glb_drop_vars <- c(NULL) # or c("<col_name>")
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
glb_assign_pairs_lst <- NULL;
# glb_assign_pairs_lst[["<var1>"]] <- list(from=c(NA),
# to=c("NA.my"))
glb_assign_vars <- names(glb_assign_pairs_lst)
glb_transform_lst <- NULL;
# glb_transform_lst[["<var>"]] <- list(
# mapfn=function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# , sfx=".my.fctr")
# mapfn(glb_allobs_df$<var>)
# glb_transform_lst[["<var1>"]] <- glb_transform_lst[["<var2>"]]
# Add logs of numerics that are not distributed normally -> do automatically ???
glb_transform_vars <- names(glb_transform_lst)
glb_date_vars <- NULL # or c("<date_var>")
glb_date_fmts <- list(); #glb_date_fmts[["<date_var>"]] <- "%m/%e/%y"
glb_date_tzs <- list(); #glb_date_tzs[["<date_var>"]] <- "America/New_York"
#grep("America/New", OlsonNames(), value=TRUE)
glb_txt_vars <- NULL # or c("<txt_var1>", "<txt_var2>")
#Sys.setlocale("LC_ALL", "C") # For english
glb_append_stop_words <- list()
# Remember to use unstemmed words
#orderBy(~ -cor.y.abs, subset(glb_feats_df, grepl("[HSA]\\.T\\.", id) & !is.na(cor.high.X)))
#dsp_obs(Headline.contains="polit")
#subset(glb_allobs_df, H.T.compani > 0)[, c("UniqueID", "Headline", "H.T.compani")]
# glb_append_stop_words[["<txt_var1>"]] <- c(NULL
# # ,"<word1>" # <reason1>
# )
#subset(glb_allobs_df, S.T.newyorktim > 0)[, c("UniqueID", "Snippet", "S.T.newyorktim")]
#glb_txt_lst[["Snippet"]][which(glb_allobs_df$UniqueID %in% c(8394, 8317, 8339, 8350, 8307))]
glb_important_terms <- list()
# Remember to use stemmed terms
glb_sprs_thresholds <- NULL # or c(0.988, 0.970, 0.970) # Generates 29, 22, 22 terms
# Properties:
# numrows(glb_feats_df) << numrows(glb_fitobs_df)
# Select terms that appear in at least 0.2 * O(FP/FN(glb_OOBobs_df))
# numrows(glb_OOBobs_df) = 1.1 * numrows(glb_newobs_df)
names(glb_sprs_thresholds) <- glb_txt_vars
# Derived features (consolidate this with transform features ???)
glb_derive_lst <- NULL;
glb_derive_lst[["PTS.diff"]] <- list(
mapfn=function(PTS, oppPTS) { return(PTS - oppPTS) }
, args=c("PTS", "oppPTS"))
# args_lst <- NULL; for (arg in glb_derive_lst[["PTS.diff"]]$args) args_lst[[arg]] <- glb_allobs_df[, arg]; do.call(mapfn, args_lst)
# glb_derive_lst[["<var1>"]] <- glb_derive_lst[["<var2>"]]
glb_derive_vars <- names(glb_derive_lst)
# User-specified exclusions
glb_exclude_vars_as_features <- c("Team.fctr", "Playoffs")
if (glb_rsp_var_raw != glb_rsp_var)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_rsp_var_raw)
# List feats that shd be excluded due to known causation by prediction variable
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(NULL)) # or c("<col_name>")
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glb_cluster <- FALSE # or TRUE
glb_interaction_only_features <- NULL # or ???
glb_models_lst <- list(); glb_models_df <- data.frame()
# Regression
if (glb_is_regression)
glb_models_method_vctr <- c("lm", "glm", "bayesglm", "rpart", "rf") else
# Classification
if (glb_is_binomial)
glb_models_method_vctr <- c("glm", "bayesglm", "rpart", "rf") else
glb_models_method_vctr <- c("rpart", "rf")
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<col_name>")
glb_model_metric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glb_model_metric <- NULL # or "<metric_name>"
glb_model_metric_maximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glb_model_metric_smmry <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glb_model_metric_terms)
# metric <- sum(confusion_mtrx * glb_model_metric_terms) / nrow(data)
# names(metric) <- glb_model_metric
# return(metric)
# }
glb_tune_models_df <-
rbind(
#data.frame(parameter="cp", min=0.00005, max=0.00005, by=0.000005),
#seq(from=0.01, to=0.01, by=0.01)
#data.frame(parameter="mtry", min=080, max=100, by=10),
#data.frame(parameter="mtry", min=08, max=10, by=1),
data.frame(parameter="dummy", min=2, max=4, by=1)
)
# or NULL
glb_n_cv_folds <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glb_model_evl_criteria <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glb_model_evl_criteria <-
c("max.Accuracy.OOB", "max.auc.OOB", "max.Kappa.OOB", "min.aic.fit") else
glb_model_evl_criteria <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
glb_sel_mdl_id <- NULL # or "<model_id_prefix>.<model_method>"
glb_fin_mdl_id <- glb_sel_mdl_id # or "Final"
# Depict process
glb_analytics_pn <- petrinet(name="glb_analytics_pn",
trans_df=data.frame(id=1:6,
name=c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df=data.frame(
begin=c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end =c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL, "import.data")## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 11.436 NA NAStep 1.0: import data
chunk option: eval=
#glb_chunks_df <- myadd_chunk(NULL, "import.data")
glb_trnobs_df <- myimport_data(url=glb_trnng_url, comment="glb_trnobs_df",
force_header=TRUE)## [1] "Reading file ./data/NBA_train.csv..."
## [1] "dimensions of data in ./data/NBA_train.csv: 835 rows x 20 cols"
## SeasonEnd Team Playoffs W PTS oppPTS FG FGA X2P
## 1 1980 Atlanta Hawks 1 50 8573 8334 3261 7027 3248
## 2 1980 Boston Celtics 1 61 9303 8664 3617 7387 3455
## 3 1980 Chicago Bulls 0 30 8813 9035 3362 6943 3292
## 4 1980 Cleveland Cavaliers 0 37 9360 9332 3811 8041 3775
## 5 1980 Denver Nuggets 0 30 8878 9240 3462 7470 3379
## 6 1980 Detroit Pistons 0 16 8933 9609 3643 7596 3586
## X2PA X3P X3PA FT FTA ORB DRB AST STL BLK TOV
## 1 6952 13 75 2038 2645 1369 2406 1913 782 539 1495
## 2 6965 162 422 1907 2449 1227 2457 2198 809 308 1539
## 3 6668 70 275 2019 2592 1115 2465 2152 704 392 1684
## 4 7854 36 187 1702 2205 1307 2381 2108 764 342 1370
## 5 7215 83 255 1871 2539 1311 2524 2079 746 404 1533
## 6 7377 57 219 1590 2149 1226 2415 1950 783 562 1742
## SeasonEnd Team Playoffs W PTS oppPTS FG FGA X2P
## 29 1981 Detroit Pistons 0 21 8174 8692 3236 6986 3223
## 127 1985 Los Angeles Lakers 1 62 9696 9093 3952 7254 3862
## 426 1997 Chicago Bulls 1 69 8458 7572 3277 6923 2754
## 607 2004 Los Angeles Clippers 0 28 7771 8147 2817 6579 2488
## 611 2004 Milwaukee Bucks 1 41 8039 7952 2970 6650 2569
## 825 2011 New York Knicks 1 42 8734 8670 3140 6867 2375
## X2PA X3P X3PA FT FTA ORB DRB AST STL BLK TOV
## 29 6902 13 84 1689 2330 1201 2111 1819 884 492 1759
## 127 6959 90 295 1702 2232 1063 2550 2575 695 481 1537
## 426 5520 523 1403 1381 1848 1235 2461 2142 716 332 1109
## 607 5555 329 1024 1808 2302 1149 2416 1653 594 376 1344
## 611 5505 401 1145 1698 2192 960 2502 1872 554 383 1110
## 825 4786 765 2081 1689 2087 847 2470 1757 625 475 1123
## SeasonEnd Team Playoffs W PTS oppPTS FG FGA
## 830 2011 Portland Trail Blazers 1 48 7896 7771 2951 6599
## 831 2011 Sacramento Kings 0 24 8151 8589 3134 6979
## 832 2011 San Antonio Spurs 1 61 8502 8034 3148 6628
## 833 2011 Toronto Raptors 0 22 8124 8639 3144 6755
## 834 2011 Utah Jazz 0 39 8153 8303 3064 6590
## 835 2011 Washington Wizards 0 23 7977 8584 3048 6888
## X2P X2PA X3P X3PA FT FTA ORB DRB AST STL BLK TOV
## 830 2433 5096 518 1503 1476 1835 996 2230 1736 660 358 1070
## 831 2706 5702 428 1277 1455 1981 1071 2526 1675 608 391 1324
## 832 2463 4901 685 1727 1521 1984 829 2603 1836 602 372 1101
## 833 2799 5664 345 1091 1491 1976 963 2343 1795 581 350 1206
## 834 2629 5334 435 1256 1590 2061 898 2338 1921 629 484 1175
## 835 2656 5706 392 1182 1489 1999 1013 2374 1592 665 502 1258
## 'data.frame': 835 obs. of 20 variables:
## $ SeasonEnd: int 1980 1980 1980 1980 1980 1980 1980 1980 1980 1980 ...
## $ Team : chr "Atlanta Hawks" "Boston Celtics" "Chicago Bulls" "Cleveland Cavaliers" ...
## $ Playoffs : int 1 1 0 0 0 0 0 1 0 1 ...
## $ W : int 50 61 30 37 30 16 24 41 37 47 ...
## $ PTS : int 8573 9303 8813 9360 8878 8933 8493 9084 9119 8860 ...
## $ oppPTS : int 8334 8664 9035 9332 9240 9609 8853 9070 9176 8603 ...
## $ FG : int 3261 3617 3362 3811 3462 3643 3527 3599 3639 3582 ...
## $ FGA : int 7027 7387 6943 8041 7470 7596 7318 7496 7689 7489 ...
## $ X2P : int 3248 3455 3292 3775 3379 3586 3500 3495 3551 3557 ...
## $ X2PA : int 6952 6965 6668 7854 7215 7377 7197 7117 7375 7375 ...
## $ X3P : int 13 162 70 36 83 57 27 104 88 25 ...
## $ X3PA : int 75 422 275 187 255 219 121 379 314 114 ...
## $ FT : int 2038 1907 2019 1702 1871 1590 1412 1782 1753 1671 ...
## $ FTA : int 2645 2449 2592 2205 2539 2149 1914 2326 2333 2250 ...
## $ ORB : int 1369 1227 1115 1307 1311 1226 1155 1394 1398 1187 ...
## $ DRB : int 2406 2457 2465 2381 2524 2415 2437 2217 2326 2429 ...
## $ AST : int 1913 2198 2152 2108 2079 1950 2028 2149 2148 2123 ...
## $ STL : int 782 809 704 764 746 783 779 782 900 863 ...
## $ BLK : int 539 308 392 342 404 562 339 373 530 356 ...
## $ TOV : int 1495 1539 1684 1370 1533 1742 1492 1565 1517 1439 ...
## - attr(*, "comment")= chr "glb_trnobs_df"
## NULL# glb_trnobs_df <- data.frame()
# for (symbol in c("Boeing", "CocaCola", "GE", "IBM", "ProcterGamble")) {
# sym_trnobs_df <-
# myimport_data(url=gsub("IBM", symbol, glb_trnng_url), comment="glb_trnobs_df",
# force_header=TRUE)
# sym_trnobs_df$Symbol <- symbol
# glb_trnobs_df <- myrbind_df(glb_trnobs_df, sym_trnobs_df)
# }
# glb_trnobs_df <-
# glb_trnobs_df %>% dplyr::filter(Year >= 1999)
if (glb_is_separate_newent_dataset) {
glb_newobs_df <- myimport_data(url=glb_newdt_url, comment="glb_newobs_df",
force_header=TRUE)
# To make plots / stats / checks easier in chunk:inspectORexplore.data
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df);
comment(glb_allobs_df) <- "glb_allobs_df"
} else {
glb_allobs_df <- glb_trnobs_df; comment(glb_allobs_df) <- "glb_allobs_df"
if (!glb_split_entity_newent_datasets) {
stop("Not implemented yet")
glb_newobs_df <- glb_trnobs_df[sample(1:nrow(glb_trnobs_df),
max(2, nrow(glb_trnobs_df) / 1000)),]
} else if (glb_split_newdata_method == "condition") {
glb_newobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=glb_split_newdata_condition)))
glb_trnobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=paste0("!(",
glb_split_newdata_condition,
")"))))
} else if (glb_split_newdata_method == "sample") {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=(1-glb_split_newdata_size_ratio))
glb_newobs_df <- glb_trnobs_df[!split, ]
glb_trnobs_df <- glb_trnobs_df[split ,]
} else if (glb_split_newdata_method == "copy") {
glb_trnobs_df <- glb_allobs_df
comment(glb_trnobs_df) <- "glb_trnobs_df"
glb_newobs_df <- glb_allobs_df
comment(glb_newobs_df) <- "glb_newobs_df"
} else stop("glb_split_newdata_method should be %in% c('condition', 'sample', 'copy')")
comment(glb_newobs_df) <- "glb_newobs_df"
myprint_df(glb_newobs_df)
str(glb_newobs_df)
if (glb_split_entity_newent_datasets) {
myprint_df(glb_trnobs_df)
str(glb_trnobs_df)
}
} ## [1] "Reading file ./data/NBA_test.csv..."
## [1] "dimensions of data in ./data/NBA_test.csv: 28 rows x 20 cols"
## SeasonEnd Team Playoffs W PTS oppPTS FG FGA X2P
## 1 2013 Atlanta Hawks 1 44 8032 7999 3084 6644 2378
## 2 2013 Brooklyn Nets 1 49 7944 7798 2942 6544 2314
## 3 2013 Charlotte Bobcats 0 21 7661 8418 2823 6649 2354
## 4 2013 Chicago Bulls 1 45 7641 7615 2926 6698 2480
## 5 2013 Cleveland Cavaliers 0 24 7913 8297 2993 6901 2446
## 6 2013 Dallas Mavericks 0 41 8293 8342 3182 6892 2576
## X2PA X3P X3PA FT FTA ORB DRB AST STL BLK TOV
## 1 4743 706 1901 1158 1619 758 2593 2007 664 369 1219
## 2 4784 628 1760 1432 1958 1047 2460 1668 599 391 1206
## 3 5250 469 1399 1546 2060 917 2389 1587 591 479 1153
## 4 5433 446 1265 1343 1738 1026 2514 1886 588 417 1171
## 5 5320 547 1581 1380 1826 1004 2359 1694 647 334 1149
## 6 5264 606 1628 1323 1669 767 2670 1906 648 454 1144
## SeasonEnd Team Playoffs W PTS oppPTS FG FGA X2P
## 1 2013 Atlanta Hawks 1 44 8032 7999 3084 6644 2378
## 5 2013 Cleveland Cavaliers 0 24 7913 8297 2993 6901 2446
## 10 2013 Houston Rockets 1 45 8688 8403 3124 6782 2257
## 11 2013 Los Angeles Clippers 1 56 8289 7760 3160 6608 2533
## 13 2013 Memphis Grizzlies 1 56 7659 7319 2964 6679 2582
## 25 2013 San Antonio Spurs 1 58 8448 7923 3210 6675 2547
## X2PA X3P X3PA FT FTA ORB DRB AST STL BLK TOV
## 1 4743 706 1901 1158 1619 758 2593 2007 664 369 1219
## 5 5320 547 1581 1380 1826 1004 2359 1694 647 334 1149
## 10 4413 867 2369 1573 2087 909 2652 1902 679 359 1348
## 11 4856 627 1752 1342 1888 938 2475 1958 784 461 1197
## 13 5572 382 1107 1349 1746 1059 2445 1715 703 436 1144
## 25 4911 663 1764 1365 1725 666 2721 2058 695 446 1206
## SeasonEnd Team Playoffs W PTS oppPTS FG FGA X2P
## 23 2013 Portland Trail Blazers 0 33 7995 8255 3009 6715 2336
## 24 2013 Sacramento Kings 0 28 8219 8619 3086 6904 2476
## 25 2013 San Antonio Spurs 1 58 8448 7923 3210 6675 2547
## 26 2013 Toronto Raptors 0 34 7971 8092 2979 6685 2408
## 27 2013 Utah Jazz 0 43 8038 8045 3046 6710 2539
## 28 2013 Washington Wizards 0 29 7644 7852 2910 6693 2365
## X2PA X3P X3PA FT FTA ORB DRB AST STL BLK TOV
## 23 4811 673 1904 1304 1680 874 2474 1784 538 353 1203
## 24 5223 610 1681 1437 1869 943 2385 1708 671 342 1199
## 25 4911 663 1764 1365 1725 666 2721 2058 695 446 1206
## 26 5020 571 1665 1442 1831 871 2426 1765 595 392 1124
## 27 5325 507 1385 1439 1883 989 2457 1859 690 515 1210
## 28 5198 545 1495 1279 1746 887 2652 1775 598 376 1238
## 'data.frame': 28 obs. of 20 variables:
## $ SeasonEnd: int 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 ...
## $ Team : chr "Atlanta Hawks" "Brooklyn Nets" "Charlotte Bobcats" "Chicago Bulls" ...
## $ Playoffs : int 1 1 0 1 0 0 1 0 1 1 ...
## $ W : int 44 49 21 45 24 41 57 29 47 45 ...
## $ PTS : int 8032 7944 7661 7641 7913 8293 8704 7778 8296 8688 ...
## $ oppPTS : int 7999 7798 8418 7615 8297 8342 8287 8105 8223 8403 ...
## $ FG : int 3084 2942 2823 2926 2993 3182 3339 2979 3130 3124 ...
## $ FGA : int 6644 6544 6649 6698 6901 6892 6983 6638 6840 6782 ...
## $ X2P : int 2378 2314 2354 2480 2446 2576 2818 2466 2472 2257 ...
## $ X2PA : int 4743 4784 5250 5433 5320 5264 5465 5198 5208 4413 ...
## $ X3P : int 706 628 469 446 547 606 521 513 658 867 ...
## $ X3PA : int 1901 1760 1399 1265 1581 1628 1518 1440 1632 2369 ...
## $ FT : int 1158 1432 1546 1343 1380 1323 1505 1307 1378 1573 ...
## $ FTA : int 1619 1958 2060 1738 1826 1669 2148 1870 1744 2087 ...
## $ ORB : int 758 1047 917 1026 1004 767 1092 991 885 909 ...
## $ DRB : int 2593 2460 2389 2514 2359 2670 2601 2463 2801 2652 ...
## $ AST : int 2007 1668 1587 1886 1694 1906 2002 1742 1845 1902 ...
## $ STL : int 664 599 591 588 647 648 762 574 567 679 ...
## $ BLK : int 369 391 479 417 334 454 533 400 346 359 ...
## $ TOV : int 1219 1206 1153 1171 1149 1144 1253 1241 1236 1348 ...
## - attr(*, "comment")= chr "glb_newobs_df"
## NULLif ((num_nas <- sum(is.na(glb_trnobs_df[, glb_rsp_var_raw]))) > 0)
stop("glb_trnobs_df$", glb_rsp_var_raw, " contains NAs for ", num_nas, " obs")
if (nrow(glb_trnobs_df) == nrow(glb_allobs_df))
warning("glb_trnobs_df same as glb_allobs_df")
if (nrow(glb_newobs_df) == nrow(glb_allobs_df))
warning("glb_newobs_df same as glb_allobs_df")
if (length(glb_drop_vars) > 0) {
warning("dropping vars: ", paste0(glb_drop_vars, collapse=", "))
glb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df), glb_drop_vars)]
glb_trnobs_df <- glb_trnobs_df[, setdiff(names(glb_trnobs_df), glb_drop_vars)]
glb_newobs_df <- glb_newobs_df[, setdiff(names(glb_newobs_df), glb_drop_vars)]
}
#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Check for duplicates in glb_id_var
if (length(glb_id_var) == 0) {
warning("using .rownames as identifiers for observations")
glb_allobs_df$.rownames <- rownames(glb_allobs_df)
glb_trnobs_df$.rownames <- rownames(glb_trnobs_df)
glb_newobs_df$.rownames <- rownames(glb_newobs_df)
glb_id_var <- ".rownames"
}## Warning: using .rownames as identifiers for observationsif (sum(duplicated(glb_allobs_df[, glb_id_var, FALSE])) > 0)
stop(glb_id_var, " duplicated in glb_allobs_df")
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_id_var)
# Combine trnent & newent into glb_allobs_df for easier manipulation
glb_trnobs_df$.src <- "Train"; glb_newobs_df$.src <- "Test";
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, ".src")
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df)
comment(glb_allobs_df) <- "glb_allobs_df"
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_trnobs_df <- glb_newobs_df <- NULL
glb_chunks_df <- myadd_chunk(glb_chunks_df, "inspect.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 11.436 11.902 0.466
## 2 inspect.data 2 0 11.902 NA NAStep 2.0: inspect data
#print(str(glb_allobs_df))
#View(glb_allobs_df)
dsp_class_dstrb <- function(var) {
xtab_df <- mycreate_xtab_df(glb_allobs_df, c(".src", var))
rownames(xtab_df) <- xtab_df$.src
xtab_df <- subset(xtab_df, select=-.src)
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Performed repeatedly in other chunks
glb_chk_data <- function() {
# Histogram of predictor in glb_trnobs_df & glb_newobs_df
print(myplot_histogram(glb_allobs_df, glb_rsp_var_raw) + facet_wrap(~ .src))
if (glb_is_classification)
dsp_class_dstrb(var=ifelse(glb_rsp_var %in% names(glb_allobs_df),
glb_rsp_var, glb_rsp_var_raw))
mycheck_problem_data(glb_allobs_df)
}
glb_chk_data()## Warning in loop_apply(n, do.ply): position_stack requires constant width:
## output may be incorrect## Warning in loop_apply(n, do.ply): position_stack requires constant width:
## output may be incorrect
## [1] "numeric data missing in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ 0s in glb_allobs_df: "
## Playoffs
## 369
## [1] "numeric data w/ Infs in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ NaNs in glb_allobs_df: "
## named integer(0)
## [1] "string data missing in glb_allobs_df: "
## Team .rownames
## 0 0# Create new features that help diagnostics
if (!is.null(glb_map_rsp_raw_to_var)) {
glb_allobs_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_allobs_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_allobs_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
if (glb_is_classification) dsp_class_dstrb(glb_rsp_var)
}
# Convert dates to numbers
# typically, dates come in as chars;
# so this must be done before converting chars to factors
myextract_dates_df <- function(df, vars, id_vars, rsp_var) {
keep_feats <- c(NULL)
for (var in vars) {
dates_df <- df[, id_vars, FALSE]
dates_df[, rsp_var] <- df[, rsp_var, FALSE]
#dates_df <- data.frame(.date=strptime(df[, var], "%Y-%m-%d %H:%M:%S"))
dates_df <- cbind(dates_df, data.frame(.date=strptime(df[, var],
glb_date_fmts[[var]], tz=glb_date_tzs[[var]])))
# print(dates_df[is.na(dates_df$.date), c("ID", "Arrest.fctr", ".date")])
# print(glb_allobs_df[is.na(dates_df$.date), c("ID", "Arrest.fctr", "Date")])
# print(head(glb_allobs_df[grepl("4/7/02 .:..", glb_allobs_df$Date), c("ID", "Arrest.fctr", "Date")]))
# print(head(strptime(glb_allobs_df[grepl("4/7/02 .:..", glb_allobs_df$Date), "Date"], "%m/%e/%y %H:%M"))
# Wrong data during EST->EDT transition
# tmp <- strptime("4/7/02 2:00","%m/%e/%y %H:%M:%S"); print(tmp); print(is.na(tmp))
# dates_df[dates_df$ID == 2068197, .date] <- tmp
# grep("(.*?) 2:(.*)", glb_allobs_df[is.na(dates_df$.date), "Date"], value=TRUE)
# dates_df[is.na(dates_df$.date), ".date"] <-
# data.frame(.date=strptime(gsub("(.*?) 2:(.*)", "\\1 3:\\2",
# glb_allobs_df[is.na(dates_df$.date), "Date"]), "%m/%e/%y %H:%M"))$.date
if (sum(is.na(dates_df$.date)) > 0) {
stop("NA POSIX dates for ", var)
print(df[is.na(dates_df$.date), c(id_vars, rsp_var, var)])
}
.date <- dates_df$.date
dates_df[, paste0(var, ".POSIX")] <- .date
dates_df[, paste0(var, ".year")] <- as.numeric(format(.date, "%Y"))
dates_df[, paste0(var, ".year.fctr")] <- as.factor(format(.date, "%Y"))
dates_df[, paste0(var, ".month")] <- as.numeric(format(.date, "%m"))
dates_df[, paste0(var, ".month.fctr")] <- as.factor(format(.date, "%m"))
dates_df[, paste0(var, ".date")] <- as.numeric(format(.date, "%d"))
dates_df[, paste0(var, ".date.fctr")] <-
cut(as.numeric(format(.date, "%d")), 5) # by month week
dates_df[, paste0(var, ".juliandate")] <- as.numeric(format(.date, "%j"))
# wkday Sun=0; Mon=1; ...; Sat=6
dates_df[, paste0(var, ".wkday")] <- as.numeric(format(.date, "%w"))
dates_df[, paste0(var, ".wkday.fctr")] <- as.factor(format(.date, "%w"))
# Get US Federal Holidays for relevant years
require(XML)
doc.html = htmlTreeParse('http://about.usps.com/news/events-calendar/2012-federal-holidays.htm', useInternal = TRUE)
# # Extract all the paragraphs (HTML tag is p, starting at
# # the root of the document). Unlist flattens the list to
# # create a character vector.
# doc.text = unlist(xpathApply(doc.html, '//p', xmlValue))
# # Replace all \n by spaces
# doc.text = gsub('\\n', ' ', doc.text)
# # Join all the elements of the character vector into a single
# # character string, separated by spaces
# doc.text = paste(doc.text, collapse = ' ')
# parse the tree by tables
txt <- unlist(strsplit(xpathSApply(doc.html, "//*/table", xmlValue), "\n"))
# do some clean up with regular expressions
txt <- grep("day, ", txt, value=TRUE)
txt <- trimws(gsub("(.*?)day, (.*)", "\\2", txt))
# txt <- gsub("\t","",txt)
# txt <- sub("^[[:space:]]*(.*?)[[:space:]]*$", "\\1", txt, perl=TRUE)
# txt <- txt[!(txt %in% c("", "|"))]
hldays <- strptime(paste(txt, ", 2012", sep=""), "%B %e, %Y")
dates_df[, paste0(var, ".hlday")] <-
ifelse(format(.date, "%Y-%m-%d") %in% hldays, 1, 0)
# NYState holidays 1.9., 13.10., 11.11., 27.11., 25.12.
dates_df[, paste0(var, ".wkend")] <- as.numeric(
(dates_df[, paste0(var, ".wkday")] %in% c(0, 6)) |
dates_df[, paste0(var, ".hlday")] )
dates_df[, paste0(var, ".hour")] <- as.numeric(format(.date, "%H"))
dates_df[, paste0(var, ".hour.fctr")] <-
if (length(unique(vals <- as.numeric(format(.date, "%H")))) <= 1)
vals else cut(vals, 3) # by work-shift
dates_df[, paste0(var, ".minute")] <- as.numeric(format(.date, "%M"))
dates_df[, paste0(var, ".minute.fctr")] <-
if (length(unique(vals <- as.numeric(format(.date, "%M")))) <= 1)
vals else cut(vals, 4) # by quarter-hours
dates_df[, paste0(var, ".second")] <- as.numeric(format(.date, "%S"))
dates_df[, paste0(var, ".second.fctr")] <-
if (length(unique(vals <- as.numeric(format(.date, "%S")))) <= 1)
vals else cut(vals, 4) # by quarter-minutes
dates_df[, paste0(var, ".day.minutes")] <-
60 * dates_df[, paste0(var, ".hour")] +
dates_df[, paste0(var, ".minute")]
if ((unq_vals_n <- length(unique(dates_df[, paste0(var, ".day.minutes")]))) > 1) {
max_degree <- min(unq_vals_n, 5)
dates_df[, paste0(var, ".day.minutes.poly.", 1:max_degree)] <-
as.matrix(poly(dates_df[, paste0(var, ".day.minutes")], max_degree))
} else max_degree <- 0
# print(gp <- myplot_box(df=dates_df, ycol_names="PubDate.day.minutes",
# xcol_name=rsp_var))
# print(gp <- myplot_scatter(df=dates_df, xcol_name=".rownames",
# ycol_name="PubDate.day.minutes", colorcol_name=rsp_var))
# print(gp <- myplot_scatter(df=dates_df, xcol_name="PubDate.juliandate",
# ycol_name="PubDate.day.minutes.poly.1", colorcol_name=rsp_var))
# print(gp <- myplot_scatter(df=dates_df, xcol_name="PubDate.day.minutes",
# ycol_name="PubDate.day.minutes.poly.4", colorcol_name=rsp_var))
#
# print(gp <- myplot_scatter(df=dates_df, xcol_name="PubDate.juliandate",
# ycol_name="PubDate.day.minutes", colorcol_name=rsp_var, smooth=TRUE))
# print(gp <- myplot_scatter(df=dates_df, xcol_name="PubDate.juliandate",
# ycol_name="PubDate.day.minutes.poly.4", colorcol_name=rsp_var, smooth=TRUE))
# print(gp <- myplot_scatter(df=dates_df, xcol_name="PubDate.juliandate",
# ycol_name=c("PubDate.day.minutes", "PubDate.day.minutes.poly.4"),
# colorcol_name=rsp_var))
# print(gp <- myplot_scatter(df=subset(dates_df, Popular.fctr=="Y"),
# xcol_name=paste0(var, ".juliandate"),
# ycol_name=paste0(var, ".day.minutes", colorcol_name=rsp_var))
# print(gp <- myplot_box(df=dates_df, ycol_names=paste0(var, ".hour"),
# xcol_name=rsp_var))
# print(gp <- myplot_bar(df=dates_df, ycol_names=paste0(var, ".hour.fctr"),
# xcol_name=rsp_var,
# colorcol_name=paste0(var, ".hour.fctr")))
keep_feats <- paste(var,
c(".POSIX", ".year.fctr", ".month.fctr", ".date.fctr", ".wkday.fctr",
".wkend", ".hour.fctr", ".minute.fctr", ".second.fctr"), sep="")
if (max_degree > 0)
keep_feats <- union(keep_feats, paste(var,
paste0(".day.minutes.poly.", 1:max_degree), sep=""))
keep_feats <- intersect(keep_feats, names(dates_df))
}
#myprint_df(dates_df)
return(dates_df[, keep_feats])
}
if (!is.null(glb_date_vars)) {
glb_allobs_df <- cbind(glb_allobs_df,
myextract_dates_df(df=glb_allobs_df, vars=glb_date_vars,
id_vars=glb_id_var, rsp_var=glb_rsp_var))
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
paste(glb_date_vars, c("", ".POSIX"), sep=""))
for (feat in glb_date_vars) {
glb_allobs_df <- orderBy(reformulate(paste0(feat, ".POSIX")), glb_allobs_df)
# print(myplot_scatter(glb_allobs_df, xcol_name=paste0(feat, ".POSIX"),
# ycol_name=glb_rsp_var, colorcol_name=glb_rsp_var))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >=
strptime("2012-12-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".POSIX"),
ycol_name=glb_rsp_var, colorcol_name=paste0(feat, ".wkend")))
# Create features that measure the gap between previous timestamp in the data
require(zoo)
z <- zoo(as.numeric(as.POSIXlt(glb_allobs_df[, paste0(feat, ".POSIX")])))
glb_allobs_df[, paste0(feat, ".zoo")] <- z
print(head(glb_allobs_df[, c(glb_id_var, feat, paste0(feat, ".zoo"))]))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >
strptime("2012-10-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".zoo"), ycol_name=glb_rsp_var,
colorcol_name=glb_rsp_var))
b <- zoo(, seq(nrow(glb_allobs_df)))
last1 <- as.numeric(merge(z-lag(z, -1), b, all=TRUE)); last1[is.na(last1)] <- 0
glb_allobs_df[, paste0(feat, ".last1.log")] <- log(1 + last1)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last1.log")] > 0, ],
ycol_names=paste0(feat, ".last1.log"),
xcol_name=glb_rsp_var))
last10 <- as.numeric(merge(z-lag(z, -10), b, all=TRUE)); last10[is.na(last10)] <- 0
glb_allobs_df[, paste0(feat, ".last10.log")] <- log(1 + last10)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last10.log")] > 0, ],
ycol_names=paste0(feat, ".last10.log"),
xcol_name=glb_rsp_var))
last100 <- as.numeric(merge(z-lag(z, -100), b, all=TRUE)); last100[is.na(last100)] <- 0
glb_allobs_df[, paste0(feat, ".last100.log")] <- log(1 + last100)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last100.log")] > 0, ],
ycol_names=paste0(feat, ".last100.log"),
xcol_name=glb_rsp_var))
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(paste0(feat, ".zoo")))
# all2$last3 = as.numeric(merge(z-lag(z, -3), b, all = TRUE))
# all2$last5 = as.numeric(merge(z-lag(z, -5), b, all = TRUE))
# all2$last10 = as.numeric(merge(z-lag(z, -10), b, all = TRUE))
# all2$last20 = as.numeric(merge(z-lag(z, -20), b, all = TRUE))
# all2$last50 = as.numeric(merge(z-lag(z, -50), b, all = TRUE))
#
#
# # order table
# all2 = all2[order(all2$id),]
#
# ## fill in NAs
# # count averages
# na.avg = all2 %>% group_by(weekend, hour) %>% dplyr::summarise(
# last1=mean(last1, na.rm=TRUE),
# last3=mean(last3, na.rm=TRUE),
# last5=mean(last5, na.rm=TRUE),
# last10=mean(last10, na.rm=TRUE),
# last20=mean(last20, na.rm=TRUE),
# last50=mean(last50, na.rm=TRUE)
# )
#
# # fill in averages
# na.merge = merge(all2, na.avg, by=c("weekend","hour"))
# na.merge = na.merge[order(na.merge$id),]
# for(i in c("last1", "last3", "last5", "last10", "last20", "last50")) {
# y = paste0(i, ".y")
# idx = is.na(all2[[i]])
# all2[idx,][[i]] <- na.merge[idx,][[y]]
# }
# rm(na.avg, na.merge, b, i, idx, n, pd, sec, sh, y, z)
}
}
# check distribution of all numeric data
dsp_numeric_feats_dstrb <- function(feats_vctr) {
for (feat in feats_vctr) {
print(sprintf("feat: %s", feat))
if (glb_is_regression)
gp <- myplot_scatter(df=glb_allobs_df, ycol_name=glb_rsp_var, xcol_name=feat,
smooth=TRUE)
if (glb_is_classification)
gp <- myplot_box(df=glb_allobs_df, ycol_names=feat, xcol_name=glb_rsp_var)
if (inherits(glb_allobs_df[, feat], "factor"))
gp <- gp + facet_wrap(reformulate(feat))
print(gp)
}
}
# dsp_numeric_vars_dstrb(setdiff(names(glb_allobs_df),
# union(myfind_chr_cols_df(glb_allobs_df),
# c(glb_rsp_var_raw, glb_rsp_var))))
add_new_diag_feats <- function(obs_df, ref_df=glb_allobs_df) {
require(plyr)
obs_df <- mutate(obs_df,
# <col_name>.NA=is.na(<col_name>),
# <col_name>.fctr=factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# <col_name>.fctr=relevel(factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# "<ref_val>"),
# <col2_name>.fctr=relevel(factor(ifelse(<col1_name> == <val>, "<oth_val>", "<ref_val>")),
# as.factor(c("R", "<ref_val>")),
# ref="<ref_val>"),
# This doesn't work - use sapply instead
# <col_name>.fctr_num=grep(<col_name>, levels(<col_name>.fctr)),
#
# Date.my=as.Date(strptime(Date, "%m/%d/%y %H:%M")),
# Year=year(Date.my),
# Month=months(Date.my),
# Weekday=weekdays(Date.my)
# <col_name>=<table>[as.character(<col2_name>)],
# <col_name>=as.numeric(<col2_name>),
# <col_name> = trunc(<col2_name> / 100),
.rnorm = rnorm(n=nrow(obs_df))
)
# If levels of a factor are different across obs_df & glb_newobs_df; predict.glm fails
# Transformations not handled by mutate
# obs_df$<col_name>.fctr.num <- sapply(1:nrow(obs_df),
# function(row_ix) grep(obs_df[row_ix, "<col_name>"],
# levels(obs_df[row_ix, "<col_name>.fctr"])))
#print(summary(obs_df))
#print(sapply(names(obs_df), function(col) sum(is.na(obs_df[, col]))))
return(obs_df)
}
glb_allobs_df <- add_new_diag_feats(glb_allobs_df)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr
##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
##
## The following object is masked from 'package:stats':
##
## filter
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Merge some <descriptor>
# glb_allobs_df$<descriptor>.my <- glb_allobs_df$<descriptor>
# glb_allobs_df[grepl("\\bAIRPORT\\b", glb_allobs_df$<descriptor>.my),
# "<descriptor>.my"] <- "AIRPORT"
# glb_allobs_df$<descriptor>.my <-
# plyr::revalue(glb_allobs_df$<descriptor>.my, c(
# "ABANDONED BUILDING" = "OTHER",
# "##" = "##"
# ))
# print(<descriptor>_freq_df <- mycreate_sqlxtab_df(glb_allobs_df, c("<descriptor>.my")))
# # print(dplyr::filter(<descriptor>_freq_df, grepl("(MEDICAL|DENTAL|OFFICE)", <descriptor>.my)))
# # print(dplyr::filter(dplyr::select(glb_allobs_df, -<var.zoo>),
# # grepl("STORE", <descriptor>.my)))
# glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, "<descriptor>")
# Check distributions of newly transformed / extracted vars
# Enhancement: remove vars that were displayed ealier
dsp_numeric_feats_dstrb(feats_vctr=setdiff(names(glb_allobs_df),
c(myfind_chr_cols_df(glb_allobs_df), glb_rsp_var_raw, glb_rsp_var,
glb_exclude_vars_as_features)))## [1] "feat: SeasonEnd"
## [1] "feat: PTS"
## [1] "feat: oppPTS"
## [1] "feat: FG"
## [1] "feat: FGA"
## [1] "feat: X2P"
## [1] "feat: X2PA"
## [1] "feat: X3P"
## [1] "feat: X3PA"
## [1] "feat: FT"
## [1] "feat: FTA"
## [1] "feat: ORB"
## [1] "feat: DRB"
## [1] "feat: AST"
## [1] "feat: STL"
## [1] "feat: BLK"
## [1] "feat: TOV"
## [1] "feat: .rnorm"
# Convert factors to dummy variables
# Build splines require(splines); bsBasis <- bs(training$age, df=3)
#pairs(subset(glb_trnobs_df, select=-c(col_symbol)))
# Check for glb_newobs_df & glb_trnobs_df features range mismatches
# Other diagnostics:
# print(subset(glb_trnobs_df, <col1_name> == max(glb_trnobs_df$<col1_name>, na.rm=TRUE) &
# <col2_name> <= mean(glb_trnobs_df$<col1_name>, na.rm=TRUE)))
# print(glb_trnobs_df[which.max(glb_trnobs_df$<col_name>),])
# print(<col_name>_freq_glb_trnobs_df <- mycreate_tbl_df(glb_trnobs_df, "<col_name>"))
# print(which.min(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>)[, 2]))
# print(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>))
# print(table(is.na(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(table(sign(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(mycreate_xtab_df(glb_trnobs_df, <col1_name>))
# print(mycreate_xtab_df(glb_trnobs_df, c(<col1_name>, <col2_name>)))
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mycreate_xtab_df(glb_trnobs_df, c("<col1_name>", "<col2_name>")))
# <col1_name>_<col2_name>_xtab_glb_trnobs_df[is.na(<col1_name>_<col2_name>_xtab_glb_trnobs_df)] <- 0
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mutate(<col1_name>_<col2_name>_xtab_glb_trnobs_df,
# <col3_name>=(<col1_name> * 1.0) / (<col1_name> + <col2_name>)))
# print(mycreate_sqlxtab_df(glb_allobs_df, c("<col1_name>", "<col2_name>")))
# print(<col2_name>_min_entity_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>, min, na.rm=TRUE)))
# print(<col1_name>_na_by_<col2_name>_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>.NA, glb_trnobs_df$<col2_name>, mean, na.rm=TRUE)))
# Other plots:
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>"))
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>", xcol_name="<col2_name>"))
# print(myplot_line(subset(glb_trnobs_df, Symbol %in% c("CocaCola", "ProcterGamble")),
# "Date.POSIX", "StockPrice", facet_row_colnames="Symbol") +
# geom_vline(xintercept=as.numeric(as.POSIXlt("2003-03-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1983-01-01")))
# )
# print(myplot_line(subset(glb_trnobs_df, Date.POSIX > as.POSIXct("2004-01-01")),
# "Date.POSIX", "StockPrice") +
# geom_line(aes(color=Symbol)) +
# coord_cartesian(xlim=c(as.POSIXct("1990-01-01"),
# as.POSIXct("2000-01-01"))) +
# coord_cartesian(ylim=c(0, 250)) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-09-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-11-01")))
# )
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", colorcol_name="<Pred.fctr>") +
# geom_point(data=subset(glb_allobs_df, <condition>),
# mapping=aes(x=<x_var>, y=<y_var>), color="red", shape=4, size=5) +
# geom_vline(xintercept=84))
rm(srt_allobs_df, last1, last10, last100, pd)## Warning in rm(srt_allobs_df, last1, last10, last100, pd): object
## 'srt_allobs_df' not found## Warning in rm(srt_allobs_df, last1, last10, last100, pd): object 'last1'
## not found## Warning in rm(srt_allobs_df, last1, last10, last100, pd): object 'last10'
## not found## Warning in rm(srt_allobs_df, last1, last10, last100, pd): object 'last100'
## not found## Warning in rm(srt_allobs_df, last1, last10, last100, pd): object 'pd' not
## foundglb_chunks_df <- myadd_chunk(glb_chunks_df, "scrub.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 2 inspect.data 2 0 11.902 20.193 8.291
## 3 scrub.data 2 1 20.193 NA NAStep 2.1: scrub data
# Options:
# 1. Not fill missing vars
# 2. Fill missing numerics with a different algorithm
# 3. Fill missing chars with data based on clusters
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ 0s in glb_allobs_df: "
## Playoffs
## 369
## [1] "numeric data w/ Infs in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ NaNs in glb_allobs_df: "
## named integer(0)
## [1] "string data missing in glb_allobs_df: "
## Team .rownames
## 0 0# if (!is.null(glb_force_0_to_NA_vars)) {
# for (feat in glb_force_0_to_NA_vars) {
# warning("Forcing ", sum(glb_allobs_df[, feat] == 0),
# " obs with ", feat, " 0s to NAs")
# glb_allobs_df[glb_allobs_df[, feat] == 0, feat] <- NA
# }
# }
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ 0s in glb_allobs_df: "
## Playoffs
## 369
## [1] "numeric data w/ Infs in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ NaNs in glb_allobs_df: "
## named integer(0)
## [1] "string data missing in glb_allobs_df: "
## Team .rownames
## 0 0dsp_catgs <- function() {
print("NewsDesk:")
print(table(glb_allobs_df$NewsDesk))
print("SectionName:")
print(table(glb_allobs_df$SectionName))
print("SubsectionName:")
print(table(glb_allobs_df$SubsectionName))
}
# sel_obs <- function(Popular=NULL,
# NewsDesk=NULL, SectionName=NULL, SubsectionName=NULL,
# Headline.contains=NULL, Snippet.contains=NULL, Abstract.contains=NULL,
# Headline.pfx=NULL, NewsDesk.nb=NULL, .clusterid=NULL, myCategory=NULL,
# perl=FALSE) {
sel_obs <- function(vars_lst) {
tmp_df <- glb_allobs_df
# Does not work for Popular == NAs ???
if (!is.null(Popular)) {
if (is.na(Popular))
tmp_df <- tmp_df[is.na(tmp_df$Popular), ] else
tmp_df <- tmp_df[tmp_df$Popular == Popular, ]
}
if (!is.null(NewsDesk))
tmp_df <- tmp_df[tmp_df$NewsDesk == NewsDesk, ]
if (!is.null(SectionName))
tmp_df <- tmp_df[tmp_df$SectionName == SectionName, ]
if (!is.null(SubsectionName))
tmp_df <- tmp_df[tmp_df$SubsectionName == SubsectionName, ]
if (!is.null(Headline.contains))
tmp_df <-
tmp_df[grep(Headline.contains, tmp_df$Headline, perl=perl), ]
if (!is.null(Snippet.contains))
tmp_df <-
tmp_df[grep(Snippet.contains, tmp_df$Snippet, perl=perl), ]
if (!is.null(Abstract.contains))
tmp_df <-
tmp_df[grep(Abstract.contains, tmp_df$Abstract, perl=perl), ]
if (!is.null(Headline.pfx)) {
if (length(grep("Headline.pfx", names(tmp_df), fixed=TRUE, value=TRUE))
> 0) tmp_df <-
tmp_df[tmp_df$Headline.pfx == Headline.pfx, ] else
warning("glb_allobs_df does not contain Headline.pfx; ignoring that filter")
}
if (!is.null(NewsDesk.nb)) {
if (any(grepl("NewsDesk.nb", names(tmp_df), fixed=TRUE)) > 0)
tmp_df <-
tmp_df[tmp_df$NewsDesk.nb == NewsDesk.nb, ] else
warning("glb_allobs_df does not contain NewsDesk.nb; ignoring that filter")
}
if (!is.null(.clusterid)) {
if (any(grepl(".clusterid", names(tmp_df), fixed=TRUE)) > 0)
tmp_df <-
tmp_df[tmp_df$clusterid == clusterid, ] else
warning("glb_allobs_df does not contain clusterid; ignoring that filter") }
if (!is.null(myCategory)) {
if (!(myCategory %in% names(glb_allobs_df)))
tmp_df <-
tmp_df[tmp_df$myCategory == myCategory, ] else
warning("glb_allobs_df does not contain myCategory; ignoring that filter")
}
return(glb_allobs_df$UniqueID %in% tmp_df$UniqueID)
}
dsp_obs <- function(..., cols=c(NULL), all=FALSE) {
tmp_df <- glb_allobs_df[sel_obs(...),
union(c("UniqueID", "Popular", "myCategory", "Headline"), cols), FALSE]
if(all) { print(tmp_df) } else { myprint_df(tmp_df) }
}
#dsp_obs(Popular=1, NewsDesk="", SectionName="", Headline.contains="Boehner")
# dsp_obs(Popular=1, NewsDesk="", SectionName="")
# dsp_obs(Popular=NA, NewsDesk="", SectionName="")
dsp_tbl <- function(...) {
tmp_entity_df <- glb_allobs_df[sel_obs(...), ]
tmp_tbl <- table(tmp_entity_df$NewsDesk,
tmp_entity_df$SectionName,
tmp_entity_df$SubsectionName,
tmp_entity_df$Popular, useNA="ifany")
#print(names(tmp_tbl))
#print(dimnames(tmp_tbl))
print(tmp_tbl)
}
dsp_hdlxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "Headline", glb_rsp_var)))
#dsp_hdlxtab("(1914)|(1939)")
dsp_catxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# dsp_catxtab("1914)|(1939)")
# dsp_catxtab("19(14|39|64):")
# dsp_catxtab("19..:")
# Create myCategory <- NewsDesk#SectionName#SubsectionName
# Fix some data before merging categories
# glb_allobs_df[sel_obs(Headline.contains="Your Turn:", NewsDesk=""),
# "NewsDesk"] <- "Styles"
# glb_allobs_df[sel_obs(Headline.contains="School", NewsDesk="", SectionName="U.S.",
# SubsectionName=""),
# "SubsectionName"] <- "Education"
# glb_allobs_df[sel_obs(Headline.contains="Today in Small Business:", NewsDesk="Business"),
# "SectionName"] <- "Business Day"
# glb_allobs_df[sel_obs(Headline.contains="Today in Small Business:", NewsDesk="Business"),
# "SubsectionName"] <- "Small Business"
# glb_allobs_df[sel_obs(Headline.contains="Readers Respond:"),
# "SectionName"] <- "Opinion"
# glb_allobs_df[sel_obs(Headline.contains="Readers Respond:"),
# "SubsectionName"] <- "Room For Debate"
# glb_allobs_df[sel_obs(NewsDesk="Business", SectionName="", SubsectionName="", Popular=NA),
# "SubsectionName"] <- "Small Business"
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% c(7973),
# c("UniqueID", "Headline", "myCategory", "NewsDesk", "SectionName", "SubsectionName")])
#
# glb_allobs_df[sel_obs(NewsDesk="Business", SectionName="", SubsectionName=""),
# "SectionName"] <- "Technology"
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% c(5076, 5736, 5924, 5911, 6532),
# c("UniqueID", "Headline", "myCategory", "NewsDesk", "SectionName", "SubsectionName")])
#
# glb_allobs_df[sel_obs(SectionName="Health"),
# "NewsDesk"] <- "Science"
# glb_allobs_df[sel_obs(SectionName="Travel"),
# "NewsDesk"] <- "Travel"
#
# glb_allobs_df[sel_obs(SubsectionName="Fashion & Style"),
# "SectionName"] <- ""
# glb_allobs_df[sel_obs(SubsectionName="Fashion & Style"),
# "SubsectionName"] <- ""
# glb_allobs_df[sel_obs(NewsDesk="Styles", SectionName="", SubsectionName="", Popular=1),
# "SectionName"] <- "U.S."
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% c(5486),
# c("UniqueID", "Headline", "myCategory", "NewsDesk", "SectionName", "SubsectionName")])
#
# glb_allobs_df$myCategory <- paste(glb_allobs_df$NewsDesk,
# glb_allobs_df$SectionName,
# glb_allobs_df$SubsectionName,
# sep="#")
# dsp_obs( Headline.contains="Music:"
# #,NewsDesk=""
# #,SectionName=""
# #,SubsectionName="Fashion & Style"
# #,Popular=1 #NA
# ,cols= c("UniqueID", "Headline", "Popular", "myCategory",
# "NewsDesk", "SectionName", "SubsectionName"),
# all=TRUE)
# dsp_obs( Headline.contains="."
# ,NewsDesk=""
# ,SectionName="Opinion"
# ,SubsectionName=""
# #,Popular=1 #NA
# ,cols= c("UniqueID", "Headline", "Popular", "myCategory",
# "NewsDesk", "SectionName", "SubsectionName"),
# all=TRUE)
# Merge some categories
# glb_allobs_df$myCategory <-
# plyr::revalue(glb_allobs_df$myCategory, c(
# "#Business Day#Dealbook" = "Business#Business Day#Dealbook",
# "#Business Day#Small Business" = "Business#Business Day#Small Business",
# "#Crosswords/Games#" = "Business#Crosswords/Games#",
# "Business##" = "Business#Technology#",
# "#Open#" = "Business#Technology#",
# "#Technology#" = "Business#Technology#",
#
# "#Arts#" = "Culture#Arts#",
# "Culture##" = "Culture#Arts#",
#
# "#World#Asia Pacific" = "Foreign#World#Asia Pacific",
# "Foreign##" = "Foreign#World#",
#
# "#N.Y. / Region#" = "Metro#N.Y. / Region#",
#
# "#Opinion#" = "OpEd#Opinion#",
# "OpEd##" = "OpEd#Opinion#",
#
# "#Health#" = "Science#Health#",
# "Science##" = "Science#Health#",
#
# "Styles##" = "Styles##Fashion",
# "Styles#Health#" = "Science#Health#",
# "Styles#Style#Fashion & Style" = "Styles##Fashion",
#
# "#Travel#" = "Travel#Travel#",
#
# "Magazine#Magazine#" = "myOther",
# "National##" = "myOther",
# "National#U.S.#Politics" = "myOther",
# "Sports##" = "myOther",
# "Sports#Sports#" = "myOther",
# "#U.S.#" = "myOther",
#
#
# # "Business##Small Business" = "Business#Business Day#Small Business",
# #
# # "#Opinion#" = "#Opinion#Room For Debate",
# "##" = "##"
# # "Business##" = "Business#Business Day#Dealbook",
# # "Foreign#World#" = "Foreign##",
# # "#Open#" = "Other",
# # "#Opinion#The Public Editor" = "OpEd#Opinion#",
# # "Styles#Health#" = "Styles##",
# # "Styles#Style#Fashion & Style" = "Styles##",
# # "#U.S.#" = "#U.S.#Education",
# ))
# ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
# mycreate_sqlxtab_df(glb_allobs_df,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# myprint_df(ctgry_xtab_df)
# write.table(ctgry_xtab_df, paste0(glb_out_pfx, "ctgry_xtab.csv"),
# row.names=FALSE)
# ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
# myCategory + NewsDesk + SectionName + SubsectionName ~
# Popular.fctr, sum, value.var=".n"))
# myprint_df(ctgry_cast_df)
# write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_cast.csv"),
# row.names=FALSE)
# print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df[, glb_rsp_var],
# useNA="ifany"))
dsp_chisq.test <- function(...) {
sel_df <- glb_allobs_df[sel_obs(...) &
!is.na(glb_allobs_df$Popular), ]
sel_df$.marker <- 1
ref_df <- glb_allobs_df[!is.na(glb_allobs_df$Popular), ]
mrg_df <- merge(ref_df[, c(glb_id_var, "Popular")],
sel_df[, c(glb_id_var, ".marker")], all.x=TRUE)
mrg_df[is.na(mrg_df)] <- 0
print(mrg_tbl <- table(mrg_df$.marker, mrg_df$Popular))
print("Rows:Selected; Cols:Popular")
#print(mrg_tbl)
print(chisq.test(mrg_tbl))
}
# dsp_chisq.test(Headline.contains="[Ee]bola")
# dsp_chisq.test(Snippet.contains="[Ee]bola")
# dsp_chisq.test(Abstract.contains="[Ee]bola")
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola"), ],
# c(glb_rsp_var, "NewsDesk", "SectionName", "SubsectionName")))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName))
# print(table(glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# glb_allobs_df$myCategory.fctr <- as.factor(glb_allobs_df$myCategory)
# glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName"))
# Copy Headline into Snipper & Abstract if they are empty
# print(glb_allobs_df[nchar(glb_allobs_df[, "Snippet"]) == 0, c("Headline", "Snippet")])
# print(glb_allobs_df[glb_allobs_df$Headline == glb_allobs_df$Snippet,
# c("UniqueID", "Headline", "Snippet")])
# glb_allobs_df[nchar(glb_allobs_df[, "Snippet"]) == 0, "Snippet"] <-
# glb_allobs_df[nchar(glb_allobs_df[, "Snippet"]) == 0, "Headline"]
#
# print(glb_allobs_df[nchar(glb_allobs_df[, "Abstract"]) == 0, c("Headline", "Abstract")])
# print(glb_allobs_df[glb_allobs_df$Headline == glb_allobs_df$Abstract,
# c("UniqueID", "Headline", "Abstract")])
# glb_allobs_df[nchar(glb_allobs_df[, "Abstract"]) == 0, "Abstract"] <-
# glb_allobs_df[nchar(glb_allobs_df[, "Abstract"]) == 0, "Headline"]
# WordCount_0_df <- subset(glb_allobs_df, WordCount == 0)
# table(WordCount_0_df$Popular, WordCount_0_df$WordCount, useNA="ifany")
# myprint_df(WordCount_0_df[,
# c("UniqueID", "Popular", "WordCount", "Headline")])Step 2.1: scrub data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "transform.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 3 scrub.data 2 1 20.193 22.958 2.765
## 4 transform.data 2 2 22.959 NA NA### Mapping dictionary
#sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_map_vars)) {
for (feat in glb_map_vars) {
map_df <- myimport_data(url=glb_map_urls[[feat]],
comment="map_df",
print_diagn=TRUE)
glb_allobs_df <- mymap_codes(glb_allobs_df, feat, names(map_df)[2],
map_df, map_join_col_name=names(map_df)[1],
map_tgt_col_name=names(map_df)[2])
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_map_vars)
}
### Forced Assignments
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (feat in glb_assign_vars) {
new_feat <- paste0(feat, ".my")
print(sprintf("Forced Assignments for: %s -> %s...", feat, new_feat))
glb_allobs_df[, new_feat] <- glb_allobs_df[, feat]
pairs <- glb_assign_pairs_lst[[feat]]
for (pair_ix in 1:length(pairs$from)) {
if (is.na(pairs$from[pair_ix]))
nobs <- nrow(filter(glb_allobs_df,
is.na(eval(parse(text=feat),
envir=glb_allobs_df)))) else
nobs <- sum(glb_allobs_df[, feat] == pairs$from[pair_ix])
#nobs <- nrow(filter(glb_allobs_df, is.na(Married.fctr))) ; print(nobs)
if ((is.na(pairs$from[pair_ix])) && (is.na(pairs$to[pair_ix])))
stop("what are you trying to do ???")
if (is.na(pairs$from[pair_ix]))
glb_allobs_df[is.na(glb_allobs_df[, feat]), new_feat] <-
pairs$to[pair_ix] else
glb_allobs_df[glb_allobs_df[, feat] == pairs$from[pair_ix], new_feat] <-
pairs$to[pair_ix]
print(sprintf(" %s -> %s for %s obs",
pairs$from[pair_ix], pairs$to[pair_ix], format(nobs, big.mark=",")))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_assign_vars)
}
### Transformations using mapping functions
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (feat in glb_transform_vars) {
new_feat <- paste0(feat, glb_transform_lst[[feat]]$sfx)
print(sprintf("Applying mapping function for: %s -> %s...", feat, new_feat))
glb_allobs_df[, new_feat] <- glb_transform_lst[[feat]]$mapfn(glb_allobs_df[, feat])
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_transform_vars)
}
### Derivations using mapping functions
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (new_feat in glb_derive_vars) {
print(sprintf("Creating new feature: %s...", new_feat))
args_lst <- NULL
for (arg in glb_derive_lst[[new_feat]]$args)
args_lst[[arg]] <- glb_allobs_df[, arg]
glb_allobs_df[, new_feat] <- do.call(glb_derive_lst[[new_feat]]$mapfn, args_lst)
}## [1] "Creating new feature: PTS.diff..."Step 2.2: transform data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "manage.missing.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 4 transform.data 2 2 22.959 22.991 0.032
## 5 manage.missing.data 2 3 22.991 NA NA# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# glb_trnobs_df <- na.omit(glb_trnobs_df)
# glb_newobs_df <- na.omit(glb_newobs_df)
# df[is.na(df)] <- 0
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ 0s in glb_allobs_df: "
## Playoffs
## 369
## [1] "numeric data w/ Infs in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ NaNs in glb_allobs_df: "
## named integer(0)
## [1] "string data missing in glb_allobs_df: "
## Team .rownames
## 0 0# Not refactored into mydsutils.R since glb_*_df might be reassigned
glb_impute_missing_data <- function() {
require(mice)
set.seed(glb_mice_complete.seed)
inp_impent_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
union(glb_exclude_vars_as_features, glb_rsp_var))]
print("Summary before imputation: ")
print(summary(inp_impent_df))
out_impent_df <- complete(mice(inp_impent_df))
print(summary(out_impent_df))
# complete(mice()) changes attributes of factors even though values don't change
ret_vars <- sapply(names(out_impent_df),
function(col) ifelse(!identical(out_impent_df[, col], inp_impent_df[, col]),
col, ""))
ret_vars <- ret_vars[ret_vars != ""]
return(out_impent_df[, ret_vars])
}
if (glb_impute_na_data &&
(length(myfind_numerics_missing(glb_allobs_df)) > 0) &&
(ncol(nonna_df <- glb_impute_missing_data()) > 0)) {
for (col in names(nonna_df)) {
glb_allobs_df[, paste0(col, ".nonNA")] <- nonna_df[, col]
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, col)
}
}
mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ 0s in glb_allobs_df: "
## Playoffs
## 369
## [1] "numeric data w/ Infs in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ NaNs in glb_allobs_df: "
## named integer(0)
## [1] "string data missing in glb_allobs_df: "
## Team .rownames
## 0 0Step 2.3: manage missing data
#```{r extract_features, cache=FALSE, eval=!is.null(glb_txt_vars)}
glb_chunks_df <- myadd_chunk(glb_chunks_df, "extract.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 5 manage.missing.data 2 3 22.991 23.073 0.082
## 6 extract.features 3 0 23.073 NA NAextract.features_chunk_df <- myadd_chunk(NULL, "extract.features_bgn")## label step_major step_minor bgn end elapsed
## 1 extract.features_bgn 1 0 23.079 NA NA# Options:
# Select Tf, log(1 + Tf), Tf-IDF or BM25Tf-IDf
# Create new features that help prediction
# <col_name>.lag.2 <- lag(zoo(glb_trnobs_df$<col_name>), -2, na.pad=TRUE)
# glb_trnobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
# <col_name>.lag.2 <- lag(zoo(glb_newobs_df$<col_name>), -2, na.pad=TRUE)
# glb_newobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
#
# glb_newobs_df[1, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df) - 1,
# "<col_name>"]
# glb_newobs_df[2, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df),
# "<col_name>"]
# glb_allobs_df <- mutate(glb_allobs_df,
# A.P.http=ifelse(grepl("http",Added,fixed=TRUE), 1, 0)
# )
#
# glb_trnobs_df <- mutate(glb_trnobs_df,
# )
#
# glb_newobs_df <- mutate(glb_newobs_df,
# )
# Create factors of string variables
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "factorize.str.vars"), major.inc=TRUE)## label step_major step_minor bgn end
## 1 extract.features_bgn 1 0 23.079 23.087
## 2 extract.features_factorize.str.vars 2 0 23.088 NA
## elapsed
## 1 0.008
## 2 NA#stop(here"); sav_allobs_df <- glb_allobs_df; #glb_allobs_df <- sav_allobs_df
print(str_vars <- myfind_chr_cols_df(glb_allobs_df))## Team .rownames .src
## "Team" ".rownames" ".src"if (length(str_vars <- setdiff(str_vars,
glb_exclude_vars_as_features)) > 0) {
for (var in str_vars) {
warning("Creating factors of string variable: ", var,
": # of unique values: ", length(unique(glb_allobs_df[, var])))
glb_allobs_df[, paste0(var, ".fctr")] <- factor(glb_allobs_df[, var],
as.factor(unique(glb_allobs_df[, var])))
# glb_trnobs_df[, paste0(var, ".fctr")] <- factor(glb_trnobs_df[, var],
# as.factor(unique(glb_allobs_df[, var])))
# glb_newobs_df[, paste0(var, ".fctr")] <- factor(glb_newobs_df[, var],
# as.factor(unique(glb_allobs_df[, var])))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, str_vars)
}## Warning: Creating factors of string variable: Team: # of unique values: 38if (!is.null(glb_txt_vars)) {
require(foreach)
require(gsubfn)
require(stringr)
require(tm)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text"), major.inc=TRUE)
chk_pattern_freq <- function(re_str, ignore.case=TRUE) {
match_mtrx <- str_extract_all(txt_vctr, regex(re_str, ignore_case=ignore.case),
simplify=TRUE)
match_df <- as.data.frame(match_mtrx[match_mtrx != ""])
names(match_df) <- "pattern"
return(mycreate_sqlxtab_df(match_df, "pattern"))
}
#tmp_freq_df <- chk_pattern_freq("\\bNew (\\w)+", ignore.case=FALSE)
#subset(chk_pattern_freq("\\bNew (\\w)+", ignore.case=FALSE), grepl("New [[:upper:]]", pattern))
#chk_pattern_freq("\\bnew (\\W)+")
chk_subfn <- function(pos_ix) {
re_str <- gsubfn_args_lst[["re_str"]][[pos_ix]]
print("re_str:"); print(re_str)
rp_frmla <- gsubfn_args_lst[["rp_frmla"]][[pos_ix]]
print("rp_frmla:"); print(rp_frmla, showEnv=FALSE)
tmp_vctr <- grep(re_str, txt_vctr, value=TRUE, ignore.case=TRUE)[1:5]
print("Before:")
print(tmp_vctr)
print("After:")
print(gsubfn(re_str, rp_frmla, tmp_vctr, ignore.case=TRUE))
}
#chk_subfn(1)
myapply_gsub <- function(...) {
if ((length_lst <- length(names(gsub_map_lst))) == 0)
return(txt_vctr)
for (ptn_ix in 1:length_lst) {
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
length(names(gsub_map_lst)), names(gsub_map_lst)[ptn_ix]))
txt_vctr <- gsub(names(gsub_map_lst)[ptn_ix], gsub_map_lst[[ptn_ix]],
txt_vctr, ...)
}
return(txt_vctr)
}
myapply_txtmap <- function(txt_vctr, ...) {
nrows <- nrow(glb_txt_map_df)
for (ptn_ix in 1:nrows) {
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
nrows, glb_txt_map_df[ptn_ix, "rex_str"]))
txt_vctr <- gsub(glb_txt_map_df[ptn_ix, "rex_str"],
glb_txt_map_df[ptn_ix, "rpl_str"],
txt_vctr, ...)
}
return(txt_vctr)
}
chk.equal <- function(bgn, end) {
print(all.equal(sav_txt_lst[["Headline"]][bgn:end], glb_txt_lst[["Headline"]][bgn:end]))
}
dsp.equal <- function(bgn, end) {
print(sav_txt_lst[["Headline"]][bgn:end])
print(glb_txt_lst[["Headline"]][bgn:end])
}
#sav_txt_lst <- glb_txt_lst; all.equal(sav_txt_lst, glb_txt_lst)
#all.equal(sav_txt_lst[["Headline"]][1:4200], glb_txt_lst[["Headline"]][1:4200])
#all.equal(sav_txt_lst[["Headline"]][1:2000], glb_txt_lst[["Headline"]][1:2000])
#all.equal(sav_txt_lst[["Headline"]][1:1000], glb_txt_lst[["Headline"]][1:1000])
#all.equal(sav_txt_lst[["Headline"]][1:500], glb_txt_lst[["Headline"]][1:500])
#all.equal(sav_txt_lst[["Headline"]][1:200], glb_txt_lst[["Headline"]][1:200])
#all.equal(sav_txt_lst[["Headline"]][1:100], glb_txt_lst[["Headline"]][1:100])
#chk.equal( 1, 100)
#chk.equal(51, 100)
#chk.equal(81, 100)
#chk.equal(81, 90)
#chk.equal(81, 85)
#chk.equal(86, 90)
#chk.equal(96, 100)
#dsp.equal(86, 90)
glb_txt_map_df <- read.csv("mytxt_map.csv", comment.char="#", strip.white=TRUE)
glb_txt_lst <- list();
print(sprintf("Building glb_txt_lst..."))
glb_txt_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_vctr <- glb_allobs_df[, txt_var]
# myapply_txtmap shd be created as a tm_map::content_transformer ?
#print(glb_txt_map_df)
#txt_var=glb_txt_vars[3]; txt_vctr <- glb_txt_lst[[txt_var]]
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rex_str == "\\bWall St\\.", "rex_str"])
#print(rex_str <- glb_txt_map_df[grepl("du Pont", glb_txt_map_df$rex_str), "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rpl_str == "versus", "rex_str"])
#print(tmp_vctr <- grep(rex_str, txt_vctr, value=TRUE, ignore.case=FALSE))
#ret_lst <- regexec(rex_str, txt_vctr, ignore.case=FALSE); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
#gsub(rex_str, glb_txt_map_df[glb_txt_map_df$rex_str == rex_str, "rpl_str"], tmp_vctr, ignore.case=FALSE)
#grep("Hong Hong", txt_vctr, value=TRUE)
txt_vctr <- myapply_txtmap(txt_vctr, ignore.case=FALSE)
}
names(glb_txt_lst) <- glb_txt_vars
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining Acronyms in %s:", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(tmp_vctr <- grep("[[:upper:]]\\.", txt_vctr, value=TRUE, ignore.case=FALSE))
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl("( |-)[[:upper:]]", pattern))))
print(" consider cleaning if relevant to problem domain; geography name; .n > 1")
#grep("New G", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Wins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(N|S|E|W|C)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))))
#grep("N Weaver", txt_vctr, value=TRUE, ignore.case=FALSE)
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(North|South|East|West|Central)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))))
#grep("Central (African|Bankers|Cast|Italy|Role|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("East (Africa|Berlin|London|Poland|Rivals|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("North (American|Korean|West)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("South (Pacific|Street)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Martins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
find_cmpnd_wrds <- function(txt_vctr) {
txt_corpus <- Corpus(VectorSource(txt_vctr))
txt_corpus <- tm_map(txt_corpus, tolower)
txt_corpus <- tm_map(txt_corpus, PlainTextDocument)
txt_corpus <- tm_map(txt_corpus, removePunctuation,
preserve_intra_word_dashes=TRUE)
full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTf))
print(" Full TermMatrix:"); print(full_Tf_DTM)
full_Tf_mtrx <- as.matrix(full_Tf_DTM)
rownames(full_Tf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_Tf_vctr <- colSums(full_Tf_mtrx)
names(full_Tf_vctr) <- dimnames(full_Tf_DTM)[[2]]
#grep("year", names(full_Tf_vctr), value=TRUE)
#which.max(full_Tf_mtrx[, "yearlong"])
full_Tf_df <- as.data.frame(full_Tf_vctr)
names(full_Tf_df) <- "Tf.full"
full_Tf_df$term <- rownames(full_Tf_df)
#full_Tf_df$freq.full <- colSums(full_Tf_mtrx != 0)
full_Tf_df <- orderBy(~ -Tf.full, full_Tf_df)
cmpnd_Tf_df <- full_Tf_df[grep("-", full_Tf_df$term, value=TRUE) ,]
filter_df <- read.csv("mytxt_compound.csv", comment.char="#", strip.white=TRUE)
cmpnd_Tf_df$filter <- FALSE
for (row_ix in 1:nrow(filter_df))
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "filter"] <-
grepl(filter_df[row_ix, "rex_str"],
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "term"], ignore.case=TRUE)
cmpnd_Tf_df <- subset(cmpnd_Tf_df, !filter)
# Bug in tm_map(txt_corpus, removePunctuation, preserve_intra_word_dashes=TRUE) ???
# "net-a-porter" gets converted to "net-aporter"
#grep("net-a-porter", txt_vctr, ignore.case=TRUE, value=TRUE)
#grep("maser-laser", txt_vctr, ignore.case=TRUE, value=TRUE)
#txt_corpus[[which(grepl("net-a-porter", txt_vctr, ignore.case=TRUE))]]
#grep("\\b(across|longer)-(\\w)", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
#grep("(\\w)-(affected|term)\\b", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
print(sprintf("nrow(cmpnd_Tf_df): %d", nrow(cmpnd_Tf_df)))
myprint_df(cmpnd_Tf_df)
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text_reporting_compound_terms"), major.inc=FALSE)
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining compound terms in %s: ", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
# find_cmpnd_wrds(txt_vctr)
#grep("thirty-five", txt_vctr, ignore.case=TRUE, value=TRUE)
#rex_str <- glb_txt_map_df[grepl("hirty", glb_txt_map_df$rex_str), "rex_str"]
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "build.corpus"), major.inc=TRUE)
glb_corpus_lst <- list()
print(sprintf("Building glb_corpus_lst..."))
glb_corpus_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_corpus <- Corpus(VectorSource(glb_txt_lst[[txt_var]]))
txt_corpus <- tm_map(txt_corpus, tolower) #nuppr
txt_corpus <- tm_map(txt_corpus, PlainTextDocument)
txt_corpus <- tm_map(txt_corpus, removePunctuation) #npnct<chr_ix>
# txt-corpus <- tm_map(txt_corpus, content_transformer(function(x, pattern) gsub(pattern, "", x))
# Not to be run in production
inspect_terms <- function() {
full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTf))
print(" Full TermMatrix:"); print(full_Tf_DTM)
full_Tf_mtrx <- as.matrix(full_Tf_DTM)
rownames(full_Tf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_Tf_vctr <- colSums(full_Tf_mtrx)
names(full_Tf_vctr) <- dimnames(full_Tf_DTM)[[2]]
#grep("year", names(full_Tf_vctr), value=TRUE)
#which.max(full_Tf_mtrx[, "yearlong"])
full_Tf_df <- as.data.frame(full_Tf_vctr)
names(full_Tf_df) <- "Tf.full"
full_Tf_df$term <- rownames(full_Tf_df)
#full_Tf_df$freq.full <- colSums(full_Tf_mtrx != 0)
full_Tf_df <- orderBy(~ -Tf.full +term, full_Tf_df)
print(myplot_histogram(full_Tf_df, "Tf.full"))
myprint_df(full_Tf_df)
#txt_corpus[[which(grepl("zun", txt_vctr, ignore.case=TRUE))]]
digit_terms_df <- subset(full_Tf_df, grepl("[[:digit:]]", term))
myprint_df(digit_terms_df)
return(full_Tf_df)
}
#print("RemovePunct:"); remove_punct_Tf_df <- inspect_terms()
txt_corpus <- tm_map(txt_corpus, removeWords,
c(glb_append_stop_words[[txt_var]],
stopwords("english"))) #nstopwrds
#print("StoppedWords:"); stopped_words_Tf_df <- inspect_terms()
txt_corpus <- tm_map(txt_corpus, stemDocument) #Features for lost information: Difference/ratio in density of full_TfIdf_DTM ???
#txt_corpus <- tm_map(txt_corpus, content_transformer(stemDocument))
#print("StemmedWords:"); stemmed_words_Tf_df <- inspect_terms()
#stemmed_stopped_Tf_df <- merge(stemmed_words_Tf_df, stopped_words_Tf_df, by="term", all=TRUE, suffixes=c(".stem", ".stop"))
#myprint_df(stemmed_stopped_Tf_df)
#print(subset(stemmed_stopped_Tf_df, grepl("compan", term)))
#glb_corpus_lst[[txt_var]] <- txt_corpus
}
names(glb_corpus_lst) <- glb_txt_vars
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "extract.DTM"), major.inc=TRUE)
glb_full_DTM_lst <- list(); glb_sprs_DTM_lst <- list();
for (txt_var in glb_txt_vars) {
print(sprintf("Extracting TfIDf terms for %s...", txt_var))
txt_corpus <- glb_corpus_lst[[txt_var]]
# full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
# control=list(weighting=weightTf))
full_TfIdf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTfIdf))
sprs_TfIdf_DTM <- removeSparseTerms(full_TfIdf_DTM,
glb_sprs_thresholds[txt_var])
# glb_full_DTM_lst[[txt_var]] <- full_Tf_DTM
# glb_sprs_DTM_lst[[txt_var]] <- sprs_Tf_DTM
glb_full_DTM_lst[[txt_var]] <- full_TfIdf_DTM
glb_sprs_DTM_lst[[txt_var]] <- sprs_TfIdf_DTM
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "report.DTM"), major.inc=TRUE)
for (txt_var in glb_txt_vars) {
print(sprintf("Reporting TfIDf terms for %s...", txt_var))
full_TfIdf_DTM <- glb_full_DTM_lst[[txt_var]]
sprs_TfIdf_DTM <- glb_sprs_DTM_lst[[txt_var]]
print(" Full TermMatrix:"); print(full_TfIdf_DTM)
full_TfIdf_mtrx <- as.matrix(full_TfIdf_DTM)
rownames(full_TfIdf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_TfIdf_vctr <- colSums(full_TfIdf_mtrx)
names(full_TfIdf_vctr) <- dimnames(full_TfIdf_DTM)[[2]]
#grep("scene", names(full_TfIdf_vctr), value=TRUE)
#which.max(full_TfIdf_mtrx[, "yearlong"])
full_TfIdf_df <- as.data.frame(full_TfIdf_vctr)
names(full_TfIdf_df) <- "TfIdf.full"
full_TfIdf_df$term <- rownames(full_TfIdf_df)
full_TfIdf_df$freq.full <- colSums(full_TfIdf_mtrx != 0)
full_TfIdf_df <- orderBy(~ -TfIdf.full, full_TfIdf_df)
print(" Sparse TermMatrix:"); print(sprs_TfIdf_DTM)
sprs_TfIdf_vctr <- colSums(as.matrix(sprs_TfIdf_DTM))
names(sprs_TfIdf_vctr) <- dimnames(sprs_TfIdf_DTM)[[2]]
sprs_TfIdf_df <- as.data.frame(sprs_TfIdf_vctr)
names(sprs_TfIdf_df) <- "TfIdf.sprs"
sprs_TfIdf_df$term <- rownames(sprs_TfIdf_df)
sprs_TfIdf_df$freq.sprs <- colSums(as.matrix(sprs_TfIdf_DTM) != 0)
sprs_TfIdf_df <- orderBy(~ -TfIdf.sprs, sprs_TfIdf_df)
terms_TfIdf_df <- merge(full_TfIdf_df, sprs_TfIdf_df, all.x=TRUE)
terms_TfIdf_df$in.sprs <- !is.na(terms_TfIdf_df$freq.sprs)
plt_TfIdf_df <- subset(terms_TfIdf_df,
TfIdf.full >= min(terms_TfIdf_df$TfIdf.sprs, na.rm=TRUE))
plt_TfIdf_df$label <- ""
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "label"] <-
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"]
glb_important_terms[[txt_var]] <- union(glb_important_terms[[txt_var]],
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"])
print(myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
colorcol_name="in.sprs") +
geom_text(aes(label=label), color="Black", size=3.5))
melt_TfIdf_df <- orderBy(~ -value, melt(terms_TfIdf_df, id.var="term"))
print(ggplot(melt_TfIdf_df, aes(value, color=variable)) + stat_ecdf() +
geom_hline(yintercept=glb_sprs_thresholds[txt_var],
linetype = "dotted"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, !is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(melt_TfIdf_df, "term", "value",
colorcol_name="variable"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(head(melt_TfIdf_df, 10), "term", "value",
colorcol_name="variable"))
}
# sav_full_DTM_lst <- glb_full_DTM_lst
# sav_sprs_DTM_lst <- glb_sprs_DTM_lst
# print(identical(sav_glb_corpus_lst, glb_corpus_lst))
# print(all.equal(length(sav_glb_corpus_lst), length(glb_corpus_lst)))
# print(all.equal(names(sav_glb_corpus_lst), names(glb_corpus_lst)))
# print(all.equal(sav_glb_corpus_lst[["Headline"]], glb_corpus_lst[["Headline"]]))
# print(identical(sav_full_DTM_lst, glb_full_DTM_lst))
# print(identical(sav_sprs_DTM_lst, glb_sprs_DTM_lst))
rm(full_TfIdf_mtrx, full_TfIdf_df, melt_TfIdf_df, terms_TfIdf_df)
# Create txt features
if ((length(glb_txt_vars) > 1) &&
(length(unique(pfxs <- sapply(glb_txt_vars,
function(txt) toupper(substr(txt, 1, 1))))) < length(glb_txt_vars)))
stop("Prefixes for corpus freq terms not unique: ", pfxs)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DTM"),
major.inc=TRUE)
for (txt_var in glb_txt_vars) {
print(sprintf("Binding DTM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_X_df <- as.data.frame(as.matrix(glb_sprs_DTM_lst[[txt_var]]))
colnames(txt_X_df) <- paste(txt_var_pfx, ".T.",
make.names(colnames(txt_X_df)), sep="")
rownames(txt_X_df) <- rownames(glb_allobs_df) # warning otherwise
# plt_X_df <- cbind(txt_X_df, glb_allobs_df[, c(glb_id_var, glb_rsp_var)])
# print(myplot_box(df=plt_X_df, ycol_names="H.T.today", xcol_name=glb_rsp_var))
# log_X_df <- log(1 + txt_X_df)
# colnames(log_X_df) <- paste(colnames(txt_X_df), ".log", sep="")
# plt_X_df <- cbind(log_X_df, glb_allobs_df[, c(glb_id_var, glb_rsp_var)])
# print(myplot_box(df=plt_X_df, ycol_names="H.T.today.log", xcol_name=glb_rsp_var))
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df) # TfIdf is normalized
#glb_allobs_df <- cbind(glb_allobs_df, log_X_df) # if using non-normalized metrics
}
#identical(chk_entity_df, glb_allobs_df)
#chk_entity_df <- glb_allobs_df
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DXM"),
major.inc=TRUE)
#sav_allobs_df <- glb_allobs_df
glb_punct_vctr <- c("!", "\"", "#", "\\$", "%", "&", "'",
"\\(|\\)",# "\\(", "\\)",
"\\*", "\\+", ",", "-", "\\.", "/", ":", ";",
"<|>", # "<",
"=",
# ">",
"\\?", "@", "\\[", "\\\\", "\\]", "^", "_", "`",
"\\{", "\\|", "\\}", "~")
txt_X_df <- glb_allobs_df[, c(glb_id_var, ".rnorm"), FALSE]
txt_X_df <- foreach(txt_var=glb_txt_vars, .combine=cbind) %dopar% {
#for (txt_var in glb_txt_vars) {
print(sprintf("Binding DXM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
#txt_X_df <- glb_allobs_df[, c(glb_id_var, ".rnorm"), FALSE]
txt_full_DTM_mtrx <- as.matrix(glb_full_DTM_lst[[txt_var]])
rownames(txt_full_DTM_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
#print(txt_full_DTM_mtrx[txt_full_DTM_mtrx[, "ebola"] != 0, "ebola"])
# Create <txt_var>.T.<term> for glb_important_terms
for (term in glb_important_terms[[txt_var]])
txt_X_df[, paste0(txt_var_pfx, ".T.", make.names(term))] <-
txt_full_DTM_mtrx[, term]
# Create <txt_var>.nwrds.log & .nwrds.unq.log
txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")] <-
log(1 + mycount_pattern_occ("\\w+", glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".nwrds.unq.log")] <-
log(1 + rowSums(txt_full_DTM_mtrx != 0))
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] <-
rowSums(txt_full_DTM_mtrx)
txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <-
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] /
(exp(txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")]) - 1)
txt_X_df[is.nan(txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")]),
paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <- 0
# Create <txt_var>.nchrs.log
txt_X_df[, paste0(txt_var_pfx, ".nchrs.log")] <-
log(1 + mycount_pattern_occ(".", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".nuppr.log")] <-
log(1 + mycount_pattern_occ("[[:upper:]]", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".ndgts.log")] <-
log(1 + mycount_pattern_occ("[[:digit:]]", glb_allobs_df[, txt_var]))
# Create <txt_var>.npnct?.log
# would this be faster if it's iterated over each row instead of
# each created column ???
for (punct_ix in 1:length(glb_punct_vctr)) {
# smp0 <- " "
# smp1 <- "! \" # $ % & ' ( ) * + , - . / : ; < = > ? @ [ \ ] ^ _ ` { | } ~"
# smp2 <- paste(smp1, smp1, sep=" ")
# print(sprintf("Testing %s pattern:", glb_punct_vctr[punct_ix]))
# results <- mycount_pattern_occ(glb_punct_vctr[punct_ix], c(smp0, smp1, smp2))
# names(results) <- NULL; print(results)
txt_X_df[,
paste0(txt_var_pfx, ".npnct", sprintf("%02d", punct_ix), ".log")] <-
log(1 + mycount_pattern_occ(glb_punct_vctr[punct_ix],
glb_allobs_df[, txt_var]))
}
# print(head(glb_allobs_df[glb_allobs_df[, "A.npnct23.log"] > 0,
# c("UniqueID", "Popular", "Abstract", "A.npnct23.log")]))
# Create <txt_var>.nstopwrds.log & <txt_var>ratio.nstopwrds.nwrds
stop_words_rex_str <- paste0("\\b(", paste0(c(glb_append_stop_words[[txt_var]],
stopwords("english")), collapse="|"),
")\\b")
txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] <-
log(1 + mycount_pattern_occ(stop_words_rex_str, glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".ratio.nstopwrds.nwrds")] <-
exp(txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] -
txt_X_df[, paste0(txt_var_pfx, ".nwrds", ".log")])
# Create <txt_var>.P.http
txt_X_df[, paste(txt_var_pfx, ".P.http", sep="")] <-
as.integer(0 + mycount_pattern_occ("http", glb_allobs_df[, txt_var]))
# Create user-specified pattern vectors
# <txt_var>.P.year.colon
txt_X_df[, paste0(txt_var_pfx, ".P.year.colon")] <-
as.integer(0 + mycount_pattern_occ("[0-9]{4}:", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.daily.clip.report")] <-
as.integer(0 + mycount_pattern_occ("Daily Clip Report", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.fashion.week")] <-
as.integer(0 + mycount_pattern_occ("Fashion Week", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.first.draft")] <-
as.integer(0 + mycount_pattern_occ("First Draft", glb_allobs_df[, txt_var]))
#sum(mycount_pattern_occ("Metropolitan Diary:", glb_allobs_df$Abstract) > 0)
if (txt_var %in% c("Snippet", "Abstract")) {
txt_X_df[, paste0(txt_var_pfx, ".P.metropolitan.diary.colon")] <-
as.integer(0 + mycount_pattern_occ("Metropolitan Diary:",
glb_allobs_df[, txt_var]))
}
#sum(mycount_pattern_occ("[0-9]{4}:", glb_allobs_df$Headline) > 0)
#sum(mycount_pattern_occ("Quandary(.*)(?=:)", glb_allobs_df$Headline, perl=TRUE) > 0)
#sum(mycount_pattern_occ("No Comment(.*):", glb_allobs_df$Headline) > 0)
#sum(mycount_pattern_occ("Friday Night Music:", glb_allobs_df$Headline) > 0)
if (txt_var %in% c("Headline")) {
txt_X_df[, paste0(txt_var_pfx, ".P.facts.figures")] <-
as.integer(0 + mycount_pattern_occ("Facts & Figures:", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.friday.night.music")] <-
as.integer(0 + mycount_pattern_occ("Friday Night Music", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.no.comment.colon")] <-
as.integer(0 + mycount_pattern_occ("No Comment(.*):", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.on.this.day")] <-
as.integer(0 + mycount_pattern_occ("On This Day", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.quandary")] <-
as.integer(0 + mycount_pattern_occ("Quandary(.*)(?=:)", glb_allobs_df[, txt_var], perl=TRUE))
txt_X_df[, paste0(txt_var_pfx, ".P.readers.respond")] <-
as.integer(0 + mycount_pattern_occ("Readers Respond", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.recap.colon")] <-
as.integer(0 + mycount_pattern_occ("Recap:", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.s.notebook")] <-
as.integer(0 + mycount_pattern_occ("s Notebook", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.today.in.politic")] <-
as.integer(0 + mycount_pattern_occ("Today in Politic", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.today.in.smallbusiness")] <-
as.integer(0 + mycount_pattern_occ("Today in Small Business:", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.verbatim.colon")] <-
as.integer(0 + mycount_pattern_occ("Verbatim:", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.what.we.are")] <-
as.integer(0 + mycount_pattern_occ("What We're", glb_allobs_df[, txt_var]))
}
#summary(glb_allobs_df[ ,grep("P.on.this.day", names(glb_allobs_df), value=TRUE)])
txt_X_df <- subset(txt_X_df, select=-.rnorm)
txt_X_df <- txt_X_df[, -grep(glb_id_var, names(txt_X_df), fixed=TRUE), FALSE]
#glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
}
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
#myplot_box(glb_allobs_df, "A.sum.TfIdf", glb_rsp_var)
# Generate summaries
# print(summary(glb_allobs_df))
# print(sapply(names(glb_allobs_df), function(col) sum(is.na(glb_allobs_df[, col]))))
# print(summary(glb_trnobs_df))
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(summary(glb_newobs_df))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_txt_vars)
rm(log_X_df, txt_X_df)
}
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# print(myplot_scatter(glb_trnobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
glb_full_DTM_lst, glb_sprs_DTM_lst, txt_corpus, txt_vctr)## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'corpus_lst' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'full_TfIdf_DTM' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'full_TfIdf_vctr' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'glb_full_DTM_lst' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'glb_sprs_DTM_lst' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'txt_corpus' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'txt_vctr' not foundextract.features_chunk_df <- myadd_chunk(extract.features_chunk_df, "extract.features_end",
major.inc=TRUE)## label step_major step_minor bgn end
## 2 extract.features_factorize.str.vars 2 0 23.088 23.105
## 3 extract.features_end 3 0 23.105 NA
## elapsed
## 2 0.017
## 3 NAmyplt_chunk(extract.features_chunk_df)## label step_major step_minor bgn end
## 2 extract.features_factorize.str.vars 2 0 23.088 23.105
## 1 extract.features_bgn 1 0 23.079 23.087
## elapsed duration
## 2 0.017 0.017
## 1 0.008 0.008
## [1] "Total Elapsed Time: 23.105 secs"
# if (glb_save_envir)
# save(glb_feats_df,
# glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
# file=paste0(glb_out_pfx, "extract_features_dsk.RData"))
# load(paste0(glb_out_pfx, "extract_features_dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all","data.new")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "cluster.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 6 extract.features 3 0 23.073 24.182 1.109
## 7 cluster.data 4 0 24.182 NA NAStep 4.0: cluster data
if (glb_cluster) {
require(proxy)
#require(hash)
require(dynamicTreeCut)
# glb_hash <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
# glb_hash_lst <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
#stophere; sav_allobs_df <- glb_allobs_df;
print("Clustering features: ")
print(cluster_vars <- grep("[HSA]\\.[PT]\\.", names(glb_allobs_df), value=TRUE))
#print(cluster_vars <- grep("[HSA]\\.", names(glb_allobs_df), value=TRUE))
glb_allobs_df$.clusterid <- 1
#print(max(table(glb_allobs_df$myCategory.fctr) / 20))
for (myCategory in c("##", "Business#Business Day#Dealbook", "OpEd#Opinion#",
"Styles#U.S.#", "Business#Technology#", "Science#Health#",
"Culture#Arts#")) {
ctgry_allobs_df <- glb_allobs_df[glb_allobs_df$myCategory == myCategory, ]
dstns_dist <- dist(ctgry_allobs_df[, cluster_vars], method = "cosine")
dstns_mtrx <- as.matrix(dstns_dist)
print(sprintf("max distance(%0.4f) pair:", max(dstns_mtrx)))
row_ix <- ceiling(which.max(dstns_mtrx) / ncol(dstns_mtrx))
col_ix <- which.max(dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c("UniqueID", "Popular", "myCategory", "Headline", cluster_vars)])
min_dstns_mtrx <- dstns_mtrx
diag(min_dstns_mtrx) <- 1
print(sprintf("min distance(%0.4f) pair:", min(min_dstns_mtrx)))
row_ix <- ceiling(which.min(min_dstns_mtrx) / ncol(min_dstns_mtrx))
col_ix <- which.min(min_dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c("UniqueID", "Popular", "myCategory", "Headline", cluster_vars)])
clusters <- hclust(dstns_dist, method = "ward.D2")
#plot(clusters, labels=NULL, hang=-1)
myplclust(clusters, lab.col=unclass(ctgry_allobs_df[, glb_rsp_var]))
#clusterGroups = cutree(clusters, k=7)
clusterGroups <- cutreeDynamic(clusters, minClusterSize=20, method="tree", deepSplit=0)
# Unassigned groups are labeled 0; the largest group has label 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#print(ctgry_allobs_df[which(clusterGroups == 1), c("UniqueID", "Popular", "Headline")])
#print(ctgry_allobs_df[(clusterGroups == 1) & !is.na(ctgry_allobs_df$Popular) & (ctgry_allobs_df$Popular==1), c("UniqueID", "Popular", "Headline")])
clusterGroups[clusterGroups == 0] <- 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#summary(factor(clusterGroups))
# clusterGroups <- clusterGroups +
# 100 * # has to be > max(table(glb_allobs_df$myCategory.fctr) / minClusterSize=20)
# which(levels(glb_allobs_df$myCategory.fctr) == myCategory)
# table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
# add to glb_allobs_df - then split the data again
glb_allobs_df[glb_allobs_df$myCategory==myCategory,]$.clusterid <- clusterGroups
#print(unique(glb_allobs_df$.clusterid))
#print(glb_feats_df[glb_feats_df$id == ".clusterid.fctr", ])
}
ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
mycreate_sqlxtab_df(glb_allobs_df,
c("myCategory", ".clusterid", glb_rsp_var)))
ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
myCategory + .clusterid ~
Popular.fctr, sum, value.var=".n"))
print(ctgry_cast_df)
#print(orderBy(~ myCategory -Y -NA, ctgry_cast_df))
# write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_clst.csv"),
# row.names=FALSE)
print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df$.clusterid,
glb_allobs_df[, glb_rsp_var],
useNA="ifany"))
# dsp_obs(.clusterid=1, myCategory="OpEd#Opinion#",
# cols=c("UniqueID", "Popular", "myCategory", ".clusterid", "Headline"),
# all=TRUE)
glb_allobs_df$.clusterid.fctr <- as.factor(glb_allobs_df$.clusterid)
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
".clusterid")
glb_interaction_only_features["myCategory.fctr"] <- c(".clusterid.fctr")
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
cluster_vars)
}
# Re-partition
glb_trnobs_df <- subset(glb_allobs_df, .src == "Train")
glb_newobs_df <- subset(glb_allobs_df, .src == "Test")
glb_chunks_df <- myadd_chunk(glb_chunks_df, "select.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 7 cluster.data 4 0 24.182 24.451 0.269
## 8 select.features 5 0 24.451 NA NAStep 5.0: select features
print(glb_feats_df <- myselect_features(entity_df=glb_trnobs_df,
exclude_vars_as_features=glb_exclude_vars_as_features,
rsp_var=glb_rsp_var))## id cor.y exclude.as.feat cor.y.abs
## PTS.diff PTS.diff 0.970743263 0 0.970743263
## Playoffs Playoffs 0.798675595 1 0.798675595
## DRB DRB 0.470897497 0 0.470897497
## oppPTS oppPTS -0.331572940 0 0.331572940
## AST AST 0.320051771 0 0.320051771
## PTS PTS 0.298825614 0 0.298825614
## TOV TOV -0.243185881 0 0.243185881
## FT FT 0.204906000 0 0.204906000
## BLK BLK 0.203921004 0 0.203921004
## FG FG 0.190396422 0 0.190396422
## Team.fctr Team.fctr -0.181789508 1 0.181789508
## FTA FTA 0.161887310 0 0.161887310
## X3P X3P 0.119044564 0 0.119044564
## STL STL 0.116194398 0 0.116194398
## ORB ORB -0.095736760 0 0.095736760
## X2PA X2PA -0.087036534 0 0.087036534
## X3PA X3PA 0.083286248 0 0.083286248
## FGA FGA -0.071445659 0 0.071445659
## X2P X2P 0.069278983 0 0.069278983
## .rnorm .rnorm 0.006191449 0 0.006191449
## SeasonEnd SeasonEnd 0.000000000 0 0.000000000# sav_feats_df <- glb_feats_df; glb_feats_df <- sav_feats_df
print(glb_feats_df <- orderBy(~-cor.y,
myfind_cor_features(feats_df=glb_feats_df, obs_df=glb_trnobs_df,
rsp_var=glb_rsp_var)))## Loading required package: reshape2## [1] "cor(X3P, X3PA)=0.9945"
## [1] "cor(W, X3P)=0.1190"
## [1] "cor(W, X3PA)=0.0833"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified X3PA as highly correlated with X3P## [1] "cor(X2P, X2PA)=0.9653"
## [1] "cor(W, X2P)=0.0693"
## [1] "cor(W, X2PA)=-0.0870"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified X2P as highly correlated with X2PA## [1] "cor(FT, FTA)=0.9505"
## [1] "cor(W, FT)=0.2049"
## [1] "cor(W, FTA)=0.1619"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified FTA as highly correlated with FT## [1] "cor(FG, PTS)=0.9420"
## [1] "cor(W, FG)=0.1904"
## [1] "cor(W, PTS)=0.2988"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified FG as highly correlated with PTS## [1] "cor(X2PA, X3P)=-0.9207"
## [1] "cor(W, X2PA)=-0.0870"
## [1] "cor(W, X3P)=0.1190"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified X2PA as highly correlated with X3P## [1] "cor(FGA, oppPTS)=0.8309"
## [1] "cor(W, FGA)=-0.0714"
## [1] "cor(W, oppPTS)=-0.3316"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified FGA as highly correlated with oppPTS## [1] "cor(oppPTS, PTS)=0.7891"
## [1] "cor(W, oppPTS)=-0.3316"
## [1] "cor(W, PTS)=0.2988"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified PTS as highly correlated with oppPTS## id cor.y exclude.as.feat cor.y.abs cor.high.X freqRatio
## 13 PTS.diff 0.970743263 0 0.970743263 <NA> 1.200000
## 11 Playoffs 0.798675595 1 0.798675595 <NA> 1.352113
## 4 DRB 0.470897497 0 0.470897497 <NA> 1.428571
## 2 AST 0.320051771 0 0.320051771 <NA> 1.000000
## 12 PTS 0.298825614 0 0.298825614 oppPTS 1.000000
## 7 FT 0.204906000 0 0.204906000 <NA> 1.166667
## 3 BLK 0.203921004 0 0.203921004 <NA> 1.125000
## 5 FG 0.190396422 0 0.190396422 PTS 1.000000
## 8 FTA 0.161887310 0 0.161887310 FT 1.000000
## 20 X3P 0.119044564 0 0.119044564 <NA> 1.500000
## 15 STL 0.116194398 0 0.116194398 <NA> 1.000000
## 21 X3PA 0.083286248 0 0.083286248 X3P 1.000000
## 18 X2P 0.069278983 0 0.069278983 X2PA 1.200000
## 1 .rnorm 0.006191449 0 0.006191449 <NA> 1.000000
## 14 SeasonEnd 0.000000000 0 0.000000000 <NA> 1.000000
## 6 FGA -0.071445659 0 0.071445659 oppPTS 1.000000
## 19 X2PA -0.087036534 0 0.087036534 X3P 1.000000
## 10 ORB -0.095736760 0 0.095736760 <NA> 1.000000
## 16 Team.fctr -0.181789508 1 0.181789508 <NA> 1.000000
## 17 TOV -0.243185881 0 0.243185881 <NA> 1.166667
## 9 oppPTS -0.331572940 0 0.331572940 <NA> 1.000000
## percentUnique zeroVar nzv myNearZV is.cor.y.abs.low
## 13 75.089820 FALSE FALSE FALSE FALSE
## 11 0.239521 FALSE FALSE FALSE FALSE
## 4 49.700599 FALSE FALSE FALSE FALSE
## 2 62.874251 FALSE FALSE FALSE FALSE
## 12 81.197605 FALSE FALSE FALSE FALSE
## 7 57.964072 FALSE FALSE FALSE FALSE
## 3 36.526946 FALSE FALSE FALSE FALSE
## 5 70.898204 FALSE FALSE FALSE FALSE
## 8 63.473054 FALSE FALSE FALSE FALSE
## 20 57.724551 FALSE FALSE FALSE FALSE
## 15 38.562874 FALSE FALSE FALSE FALSE
## 21 77.844311 FALSE FALSE FALSE FALSE
## 18 72.934132 FALSE FALSE FALSE FALSE
## 1 100.000000 FALSE FALSE FALSE FALSE
## 14 3.712575 FALSE FALSE FALSE TRUE
## 6 76.287425 FALSE FALSE FALSE FALSE
## 19 85.748503 FALSE FALSE FALSE FALSE
## 10 51.736527 FALSE FALSE FALSE FALSE
## 16 4.431138 FALSE FALSE FALSE FALSE
## 17 53.772455 FALSE FALSE FALSE FALSE
## 9 83.353293 FALSE FALSE FALSE FALSE#subset(glb_feats_df, id %in% c("A.nuppr.log", "S.nuppr.log"))
print(myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
colorcol_name="myNearZV", jitter=TRUE) +
geom_point(aes(shape=nzv)) + xlim(-5, 25))## Warning in myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "myNearZV", : converting myNearZV to class:factor## Warning in loop_apply(n, do.ply): Removed 18 rows containing missing values
## (geom_point).## Warning in loop_apply(n, do.ply): Removed 18 rows containing missing values
## (geom_point).## Warning in loop_apply(n, do.ply): Removed 18 rows containing missing values
## (geom_point).
print(subset(glb_feats_df, myNearZV))## [1] id cor.y exclude.as.feat cor.y.abs
## [5] cor.high.X freqRatio percentUnique zeroVar
## [9] nzv myNearZV is.cor.y.abs.low
## <0 rows> (or 0-length row.names)glb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
subset(glb_feats_df, myNearZV)$id)]
if (!is.null(glb_interaction_only_features))
glb_feats_df[glb_feats_df$id %in% glb_interaction_only_features, "interaction.feat"] <-
names(glb_interaction_only_features) else
glb_feats_df$interaction.feat <- NA
mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in : "
## named integer(0)
## [1] "numeric data w/ 0s in : "
## Playoffs
## 369
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## Team .rownames
## 0 0# glb_allobs_df %>% filter(is.na(Married.fctr)) %>% tbl_df()
# glb_allobs_df %>% count(Married.fctr)
# levels(glb_allobs_df$Married.fctr)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "partition.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 8 select.features 5 0 24.451 25.144 0.693
## 9 partition.data.training 6 0 25.144 NA NAStep 6.0: partition data training
if (all(is.na(glb_newobs_df[, glb_rsp_var]))) {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=1 - (nrow(glb_newobs_df) * 1.1 / nrow(glb_trnobs_df)))
glb_fitobs_df <- glb_trnobs_df[split, ]
glb_OOBobs_df <- glb_trnobs_df[!split ,]
} else {
print(sprintf("Newdata contains non-NA data for %s; setting OOB to Newdata",
glb_rsp_var))
glb_fitobs_df <- glb_trnobs_df; glb_OOBobs_df <- glb_newobs_df
}## [1] "Newdata contains non-NA data for W; setting OOB to Newdata"if (!is.null(glb_max_fitent_obs) && (nrow(glb_fitobs_df) > glb_max_fitent_obs)) {
warning("glb_fitobs_df restricted to glb_max_fitent_obs: ",
format(glb_max_fitent_obs, big.mark=","))
org_fitent_df <- glb_fitobs_df
glb_fitobs_df <-
org_fitent_df[split <- sample.split(org_fitent_df[, glb_rsp_var_raw],
SplitRatio=glb_max_fitent_obs), ]
org_fitent_df <- NULL
}
glb_allobs_df$.lcn <- ""
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_fitobs_df[, glb_id_var], ".lcn"] <- "Fit"
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ".lcn"] <- "OOB"
dsp_class_dstrb <- function(obs_df, location_var, partition_var) {
xtab_df <- mycreate_xtab_df(obs_df, c(location_var, partition_var))
rownames(xtab_df) <- xtab_df[, location_var]
xtab_df <- xtab_df[, -grepl(location_var, names(xtab_df))]
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Ensure proper splits by glb_rsp_var_raw & user-specified feature for OOB vs. new
if (!is.null(glb_category_vars)) {
if (glb_is_classification)
dsp_class_dstrb(glb_allobs_df, ".lcn", glb_rsp_var_raw)
newent_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .src == "Test"),
glb_category_vars)
OOBobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .lcn == "OOB"),
glb_category_vars)
glb_ctgry_df <- merge(newent_ctgry_df, OOBobs_ctgry_df, by=glb_category_vars
, all=TRUE, suffixes=c(".Tst", ".OOB"))
glb_ctgry_df$.freqRatio.Tst <- glb_ctgry_df$.n.Tst / sum(glb_ctgry_df$.n.Tst, na.rm=TRUE)
glb_ctgry_df$.freqRatio.OOB <- glb_ctgry_df$.n.OOB / sum(glb_ctgry_df$.n.OOB, na.rm=TRUE)
print(orderBy(~-.freqRatio.Tst-.freqRatio.OOB, glb_ctgry_df))
}
# Run this line by line
print("glb_feats_df:"); print(dim(glb_feats_df))## [1] "glb_feats_df:"## [1] 21 12sav_feats_df <- glb_feats_df
glb_feats_df <- sav_feats_df
glb_feats_df[, "rsp_var_raw"] <- FALSE
glb_feats_df[glb_feats_df$id == glb_rsp_var_raw, "rsp_var_raw"] <- TRUE
glb_feats_df$exclude.as.feat <- (glb_feats_df$exclude.as.feat == 1)
if (!is.null(glb_id_var) && glb_id_var != ".rownames")
glb_feats_df[glb_feats_df$id %in% glb_id_var, "id_var"] <- TRUE
add_feats_df <- data.frame(id=glb_rsp_var, exclude.as.feat=TRUE, rsp_var=TRUE)
row.names(add_feats_df) <- add_feats_df$id; print(add_feats_df)## id exclude.as.feat rsp_var
## W W TRUE TRUEglb_feats_df <- myrbind_df(glb_feats_df, add_feats_df)
if (glb_id_var != ".rownames")
print(subset(glb_feats_df, rsp_var_raw | rsp_var | id_var)) else
print(subset(glb_feats_df, rsp_var_raw | rsp_var)) ## id cor.y exclude.as.feat cor.y.abs cor.high.X freqRatio percentUnique
## W W NA TRUE NA <NA> NA NA
## zeroVar nzv myNearZV is.cor.y.abs.low interaction.feat rsp_var_raw
## W NA NA NA NA NA NA
## rsp_var
## W TRUEprint("glb_feats_df vs. glb_allobs_df: "); ## [1] "glb_feats_df vs. glb_allobs_df: "print(setdiff(glb_feats_df$id, names(glb_allobs_df)))## character(0)print("glb_allobs_df vs. glb_feats_df: "); ## [1] "glb_allobs_df vs. glb_feats_df: "# Ensure these are only chr vars
print(setdiff(setdiff(names(glb_allobs_df), glb_feats_df$id),
myfind_chr_cols_df(glb_allobs_df)))## character(0)#print(setdiff(setdiff(names(glb_allobs_df), glb_exclude_vars_as_features),
# glb_feats_df$id))
print("glb_allobs_df: "); print(dim(glb_allobs_df))## [1] "glb_allobs_df: "## [1] 863 26print("glb_trnobs_df: "); print(dim(glb_trnobs_df))## [1] "glb_trnobs_df: "## [1] 835 25print("glb_fitobs_df: "); print(dim(glb_fitobs_df))## [1] "glb_fitobs_df: "## [1] 835 25print("glb_OOBobs_df: "); print(dim(glb_OOBobs_df))## [1] "glb_OOBobs_df: "## [1] 28 25print("glb_newobs_df: "); print(dim(glb_newobs_df))## [1] "glb_newobs_df: "## [1] 28 25# # Does not handle NULL or length(glb_id_var) > 1
# glb_allobs_df$.src.trn <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_trnobs_df[, glb_id_var],
# ".src.trn"] <- 1
# glb_allobs_df$.src.fit <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_fitobs_df[, glb_id_var],
# ".src.fit"] <- 1
# glb_allobs_df$.src.OOB <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_OOBobs_df[, glb_id_var],
# ".src.OOB"] <- 1
# glb_allobs_df$.src.new <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_newobs_df[, glb_id_var],
# ".src.new"] <- 1
# #print(unique(glb_allobs_df[, ".src.trn"]))
# write_cols <- c(glb_feats_df$id,
# ".src.trn", ".src.fit", ".src.OOB", ".src.new")
# glb_allobs_df <- glb_allobs_df[, write_cols]
#
# tmp_feats_df <- glb_feats_df
# tmp_entity_df <- glb_allobs_df
if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
file=paste0(glb_out_pfx, "blddfs_dsk.RData"))
# load(paste0(glb_out_pfx, "blddfs_dsk.RData"))
# if (!all.equal(tmp_feats_df, glb_feats_df))
# stop("glb_feats_df r/w not working")
# if (!all.equal(tmp_entity_df, glb_allobs_df))
# stop("glb_allobs_df r/w not working")
rm(split)## Warning in rm(split): object 'split' not foundglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 9 partition.data.training 6 0 25.144 25.441 0.297
## 10 fit.models 7 0 25.441 NA NAStep 7.0: fit models
# load(paste0(glb_out_pfx, "dsk.RData"))
# keep_cols <- setdiff(names(glb_allobs_df),
# grep("^.src", names(glb_allobs_df), value=TRUE))
# glb_trnobs_df <- glb_allobs_df[glb_allobs_df$.src.trn == 1, keep_cols]
# glb_fitobs_df <- glb_allobs_df[glb_allobs_df$.src.fit == 1, keep_cols]
# glb_OOBobs_df <- glb_allobs_df[glb_allobs_df$.src.OOB == 1, keep_cols]
# glb_newobs_df <- glb_allobs_df[glb_allobs_df$.src.new == 1, keep_cols]
#
# glb_models_lst <- list(); glb_models_df <- data.frame()
#
if (glb_is_classification && glb_is_binomial &&
(length(unique(glb_fitobs_df[, glb_rsp_var])) < 2))
stop("glb_fitobs_df$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glb_fitobs_df[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
# while(length(max_cor_y_x_vars) < 2) {
# max_cor_y_x_vars <- c(max_cor_y_x_vars, orderBy(~ -cor.y.abs,
# subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low))[3, "id"])
# }
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a lower correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Baseline
if (!is.null(glb_Baseline_mdl_var))
ret_lst <- myfit_mdl_fn(model_id="Baseline", model_method="mybaseln_classfr",
indep_vars_vctr=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
ret_lst <- myfit_mdl(model_id="MFO",
model_method=ifelse(glb_is_regression, "lm", "myMFO_classfr"),
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: MFO.lm"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.056 -9.910 0.935 9.531 31.110
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 40.99783 0.44134 92.894 <2e-16 ***
## .rnorm 0.08103 0.45344 0.179 0.858
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.75 on 833 degrees of freedom
## Multiple R-squared: 3.833e-05, Adjusted R-squared: -0.001162
## F-statistic: 0.03193 on 1 and 833 DF, p-value: 0.8582
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO.lm lm .rnorm 0 0.45
## min.elapsedtime.final max.R.sq.fit min.RMSE.fit max.R.sq.OOB
## 1 0.003 3.833404e-05 12.73295 0.0002542561
## min.RMSE.OOB max.Adj.R.sq.fit
## 1 12.84499 -0.0011621if (glb_is_classification)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
ret_lst <- myfit_mdl(model_id="Random", model_method="myrandom_classfr",
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
# Any models that have tuning parameters has "better" results with cross-validation
# (except rf) & "different" results for different outcome metrics
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Max.cor.Y.cv.0.rpart"
## [1] " indep_vars: PTS.diff, DRB"## Loading required package: rpart## Fitting cp = 0.662 on full training set## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 835
##
## CP nsplit rel error
## 1 0.6623927 0 1
##
## Node number 1: 835 observations
## mean=41, MSE=162.1341
##
## n= 835
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 835 135382 41 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.cv.0.rpart rpart PTS.diff, DRB 0
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 0.603 0.019 0
## min.RMSE.fit max.R.sq.OOB min.RMSE.OOB
## 1 12.73319 0 12.84662ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0.cp.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=0,
tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))## [1] "fitting model: Max.cor.Y.cv.0.cp.0.rpart"
## [1] " indep_vars: PTS.diff, DRB"
## Fitting cp = 0 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 835
##
## CP nsplit rel error
## 1 6.623927e-01 0 1.00000000
## 2 1.073213e-01 1 0.33760727
## 3 1.065599e-01 2 0.23028599
## 4 1.703383e-02 3 0.12372610
## 5 1.569972e-02 4 0.10669227
## 6 1.387508e-02 5 0.09099255
## 7 1.284477e-02 6 0.07711747
## 8 3.102830e-03 7 0.06427270
## 9 1.911398e-03 8 0.06116987
## 10 1.874313e-03 9 0.05925847
## 11 1.816205e-03 10 0.05738416
## 12 1.806570e-03 11 0.05556795
## 13 1.537916e-03 12 0.05376138
## 14 6.495652e-04 13 0.05222347
## 15 5.506328e-04 14 0.05157390
## 16 5.103544e-04 15 0.05102327
## 17 5.062708e-04 16 0.05051291
## 18 4.905921e-04 17 0.05000664
## 19 4.673565e-04 19 0.04902546
## 20 4.504875e-04 20 0.04855810
## 21 4.332054e-04 21 0.04810762
## 22 4.309939e-04 22 0.04767441
## 23 3.697523e-04 23 0.04724342
## 24 3.579615e-04 25 0.04650391
## 25 3.016490e-04 26 0.04614595
## 26 2.836418e-04 28 0.04554265
## 27 2.687710e-04 29 0.04525901
## 28 2.603248e-04 30 0.04499024
## 29 2.459988e-04 34 0.04394894
## 30 2.443703e-04 35 0.04370294
## 31 2.350252e-04 37 0.04321420
## 32 2.197229e-04 38 0.04297918
## 33 2.167317e-04 39 0.04275945
## 34 2.123464e-04 40 0.04254272
## 35 2.007520e-04 43 0.04190568
## 36 1.739257e-04 45 0.04150418
## 37 1.736716e-04 46 0.04133025
## 38 1.650639e-04 47 0.04115658
## 39 1.645247e-04 48 0.04099152
## 40 1.625911e-04 49 0.04082699
## 41 1.609864e-04 50 0.04066440
## 42 1.348997e-04 51 0.04050341
## 43 1.188876e-04 52 0.04036851
## 44 1.180149e-04 53 0.04024963
## 45 1.142798e-04 55 0.04001360
## 46 1.094386e-04 56 0.03989932
## 47 9.835073e-05 57 0.03978988
## 48 8.889782e-05 58 0.03969153
## 49 8.341044e-05 59 0.03960263
## 50 7.755832e-05 60 0.03951922
## 51 7.565611e-05 61 0.03944166
## 52 7.506639e-05 62 0.03936601
## 53 6.753783e-05 63 0.03929094
## 54 6.660433e-05 64 0.03922340
## 55 5.518541e-05 66 0.03909019
## 56 5.503109e-05 67 0.03903501
## 57 5.042522e-05 69 0.03892494
## 58 4.316789e-05 70 0.03887452
## 59 3.510214e-05 71 0.03883135
## 60 2.997770e-05 72 0.03879625
## 61 0.000000e+00 73 0.03876627
##
## Variable importance
## PTS.diff DRB
## 81 19
##
## Node number 1: 835 observations, complexity param=0.6623927
## mean=41, MSE=162.1341
## left son=2 (373 obs) right son=3 (462 obs)
## Primary splits:
## PTS.diff < -32.5 to the left, improve=0.6623927, (0 missing)
## DRB < 2377.5 to the left, improve=0.1549703, (0 missing)
## Surrogate splits:
## DRB < 2377.5 to the left, agree=0.668, adj=0.257, (0 split)
##
## Node number 2: 373 observations, complexity param=0.1065599
## mean=29.46649, MSE=60.08802
## left son=4 (159 obs) right son=5 (214 obs)
## Primary splits:
## PTS.diff < -359.5 to the left, improve=0.64366220, (0 missing)
## DRB < 2221.5 to the left, improve=0.09562385, (0 missing)
## Surrogate splits:
## DRB < 2364.5 to the left, agree=0.646, adj=0.17, (0 split)
##
## Node number 3: 462 observations, complexity param=0.1073213
## mean=50.31169, MSE=50.418
## left son=6 (275 obs) right son=7 (187 obs)
## Primary splits:
## PTS.diff < 316 to the left, improve=0.62376240, (0 missing)
## DRB < 2582.5 to the left, improve=0.09916884, (0 missing)
## Surrogate splits:
## DRB < 2584 to the left, agree=0.688, adj=0.23, (0 split)
##
## Node number 4: 159 observations, complexity param=0.01387508
## mean=22.25157, MSE=23.09394
## left son=8 (67 obs) right son=9 (92 obs)
## Primary splits:
## PTS.diff < -572 to the left, improve=0.5115656, (0 missing)
## DRB < 2198 to the left, improve=0.1124918, (0 missing)
## Surrogate splits:
## DRB < 2215.5 to the left, agree=0.686, adj=0.254, (0 split)
##
## Node number 5: 214 observations, complexity param=0.01569972
## mean=34.8271, MSE=20.1617
## left son=10 (77 obs) right son=11 (137 obs)
## Primary splits:
## PTS.diff < -232.5 to the left, improve=0.4926199, (0 missing)
## DRB < 2192 to the left, improve=0.0265797, (0 missing)
## Surrogate splits:
## DRB < 2192 to the left, agree=0.654, adj=0.039, (0 split)
##
## Node number 6: 275 observations, complexity param=0.01703383
## mean=45.68727, MSE=18.17129
## left son=12 (159 obs) right son=13 (116 obs)
## Primary splits:
## PTS.diff < 160 to the left, improve=0.46148200, (0 missing)
## DRB < 2581 to the left, improve=0.05347047, (0 missing)
## Surrogate splits:
## DRB < 2568.5 to the left, agree=0.618, adj=0.095, (0 split)
##
## Node number 7: 187 observations, complexity param=0.01284477
## mean=57.1123, MSE=20.14247
## left son=14 (83 obs) right son=15 (104 obs)
## Primary splits:
## PTS.diff < 449.5 to the left, improve=0.46167140, (0 missing)
## DRB < 2398.5 to the left, improve=0.04033874, (0 missing)
## Surrogate splits:
## DRB < 2387 to the left, agree=0.578, adj=0.048, (0 split)
##
## Node number 8: 67 observations, complexity param=0.001874313
## mean=18.22388, MSE=12.26331
## left son=16 (40 obs) right son=17 (27 obs)
## Primary splits:
## PTS.diff < -670.5 to the left, improve=0.3088308, (0 missing)
## DRB < 2179 to the left, improve=0.1065289, (0 missing)
##
## Node number 9: 92 observations, complexity param=0.001816205
## mean=25.18478, MSE=10.56368
## left son=18 (44 obs) right son=19 (48 obs)
## Primary splits:
## PTS.diff < -458.5 to the left, improve=0.25300120, (0 missing)
## DRB < 2223.5 to the left, improve=0.03677405, (0 missing)
## Surrogate splits:
## DRB < 2370.5 to the left, agree=0.565, adj=0.091, (0 split)
##
## Node number 10: 77 observations, complexity param=0.0006495652
## mean=30.62338, MSE=10.23478
## left son=20 (54 obs) right son=21 (23 obs)
## Primary splits:
## PTS.diff < -258.5 to the left, improve=0.11158720, (0 missing)
## DRB < 2295 to the left, improve=0.04230683, (0 missing)
## Surrogate splits:
## DRB < 2558.5 to the left, agree=0.727, adj=0.087, (0 split)
##
## Node number 11: 137 observations, complexity param=0.001911398
## mean=37.18978, MSE=10.22676
## left son=22 (61 obs) right son=23 (76 obs)
## Primary splits:
## PTS.diff < -125 to the left, improve=0.18469430, (0 missing)
## DRB < 2543.5 to the right, improve=0.01643089, (0 missing)
## Surrogate splits:
## DRB < 2543.5 to the right, agree=0.599, adj=0.098, (0 split)
##
## Node number 12: 159 observations, complexity param=0.001537916
## mean=43.21384, MSE=8.771884
## left son=24 (97 obs) right son=25 (62 obs)
## Primary splits:
## PTS.diff < 87.5 to the left, improve=0.14928060, (0 missing)
## DRB < 2395.5 to the left, improve=0.02616903, (0 missing)
## Surrogate splits:
## DRB < 2505.5 to the left, agree=0.648, adj=0.097, (0 split)
##
## Node number 13: 116 observations, complexity param=0.00180657
## mean=49.07759, MSE=11.17501
## left son=26 (67 obs) right son=27 (49 obs)
## Primary splits:
## PTS.diff < 250.5 to the left, improve=0.18867300, (0 missing)
## DRB < 2579.5 to the left, improve=0.06031685, (0 missing)
## Surrogate splits:
## DRB < 2300.5 to the right, agree=0.612, adj=0.082, (0 split)
##
## Node number 14: 83 observations, complexity param=0.0004332054
## mean=53.6988, MSE=7.583974
## left son=28 (8 obs) right son=29 (75 obs)
## Primary splits:
## DRB < 2358 to the left, improve=0.09317080, (0 missing)
## PTS.diff < 345 to the left, improve=0.08817094, (0 missing)
##
## Node number 15: 104 observations, complexity param=0.00310283
## mean=59.83654, MSE=13.44443
## left son=30 (91 obs) right son=31 (13 obs)
## Primary splits:
## PTS.diff < 696 to the left, improve=0.30042980, (0 missing)
## DRB < 2597 to the right, improve=0.01673677, (0 missing)
## Surrogate splits:
## DRB < 2737 to the left, agree=0.885, adj=0.077, (0 split)
##
## Node number 16: 40 observations, complexity param=0.0004504875
## mean=16.625, MSE=7.984375
## left son=32 (9 obs) right son=33 (31 obs)
## Primary splits:
## PTS.diff < -829.5 to the left, improve=0.1909602, (0 missing)
## DRB < 2358.5 to the left, improve=0.1878669, (0 missing)
##
## Node number 17: 27 observations, complexity param=0.0005062708
## mean=20.59259, MSE=9.20439
## left son=34 (20 obs) right son=35 (7 obs)
## Primary splits:
## DRB < 2351 to the left, improve=0.27579410, (0 missing)
## PTS.diff < -632.5 to the left, improve=0.02933362, (0 missing)
##
## Node number 18: 44 observations, complexity param=0.0001736716
## mean=23.47727, MSE=9.249483
## left son=36 (25 obs) right son=37 (19 obs)
## Primary splits:
## PTS.diff < -498.5 to the left, improve=0.05777229, (0 missing)
## DRB < 2346 to the left, improve=0.02591213, (0 missing)
## Surrogate splits:
## DRB < 2414.5 to the right, agree=0.614, adj=0.105, (0 split)
##
## Node number 19: 48 observations, complexity param=0.000268771
## mean=26.75, MSE=6.645833
## left son=38 (41 obs) right son=39 (7 obs)
## Primary splits:
## DRB < 2500 to the left, improve=0.11406510, (0 missing)
## PTS.diff < -421.5 to the left, improve=0.07594637, (0 missing)
##
## Node number 20: 54 observations, complexity param=0.0002603248
## mean=29.92593, MSE=9.994513
## left son=40 (7 obs) right son=41 (47 obs)
## Primary splits:
## PTS.diff < -275 to the right, improve=0.05527359, (0 missing)
## DRB < 2282.5 to the left, improve=0.02719884, (0 missing)
##
## Node number 21: 23 observations, complexity param=7.565611e-05
## mean=32.26087, MSE=6.975425
## left son=42 (10 obs) right son=43 (13 obs)
## Primary splits:
## DRB < 2441 to the right, improve=0.06384198, (0 missing)
## PTS.diff < -252.5 to the left, improve=0.04426378, (0 missing)
## Surrogate splits:
## PTS.diff < -236 to the right, agree=0.652, adj=0.2, (0 split)
##
## Node number 22: 61 observations, complexity param=0.0004905921
## mean=35.65574, MSE=8.979844
## left son=44 (12 obs) right son=45 (49 obs)
## Primary splits:
## DRB < 2333.5 to the left, improve=0.11712930, (0 missing)
## PTS.diff < -217.5 to the left, improve=0.02757184, (0 missing)
##
## Node number 23: 76 observations, complexity param=0.0004673565
## mean=38.42105, MSE=7.822715
## left son=46 (69 obs) right son=47 (7 obs)
## Primary splits:
## DRB < 2272.5 to the right, improve=0.10642360, (0 missing)
## PTS.diff < -80.5 to the left, improve=0.07905675, (0 missing)
##
## Node number 24: 97 observations, complexity param=0.0003697523
## mean=42.29897, MSE=6.333298
## left son=48 (48 obs) right son=49 (49 obs)
## Primary splits:
## DRB < 2418.5 to the left, improve=0.07025790, (0 missing)
## PTS.diff < 70 to the left, improve=0.03392774, (0 missing)
## Surrogate splits:
## PTS.diff < 4 to the right, agree=0.588, adj=0.167, (0 split)
##
## Node number 25: 62 observations, complexity param=0.000301649
## mean=44.64516, MSE=9.228928
## left son=50 (12 obs) right son=51 (50 obs)
## Primary splits:
## DRB < 2548 to the right, improve=0.07038449, (0 missing)
## PTS.diff < 140 to the left, improve=0.02736625, (0 missing)
## Surrogate splits:
## PTS.diff < 154.5 to the right, agree=0.823, adj=0.083, (0 split)
##
## Node number 26: 67 observations, complexity param=0.0001739257
## mean=47.83582, MSE=8.107374
## left son=52 (60 obs) right son=53 (7 obs)
## Primary splits:
## DRB < 2579.5 to the left, improve=0.04334807, (0 missing)
## PTS.diff < 235.5 to the right, improve=0.01378308, (0 missing)
##
## Node number 27: 49 observations, complexity param=0.0005506328
## mean=50.77551, MSE=10.37818
## left son=54 (22 obs) right son=55 (27 obs)
## Primary splits:
## DRB < 2455 to the left, improve=0.14659050, (0 missing)
## PTS.diff < 267.5 to the right, improve=0.04334227, (0 missing)
## Surrogate splits:
## PTS.diff < 271.5 to the left, agree=0.612, adj=0.136, (0 split)
##
## Node number 28: 8 observations
## mean=51.125, MSE=9.109375
##
## Node number 29: 75 observations, complexity param=0.0002459988
## mean=53.97333, MSE=6.639289
## left son=58 (21 obs) right son=59 (54 obs)
## Primary splits:
## PTS.diff < 345 to the left, improve=0.06688228, (0 missing)
## DRB < 2640.5 to the right, improve=0.02717955, (0 missing)
## Surrogate splits:
## DRB < 2404.5 to the left, agree=0.76, adj=0.143, (0 split)
##
## Node number 30: 91 observations, complexity param=0.0003579615
## mean=59.07692, MSE=8.906171
## left son=60 (56 obs) right son=61 (35 obs)
## Primary splits:
## PTS.diff < 582.5 to the left, improve=0.05979499, (0 missing)
## DRB < 2663.5 to the right, improve=0.03002995, (0 missing)
## Surrogate splits:
## DRB < 2277 to the right, agree=0.626, adj=0.029, (0 split)
##
## Node number 31: 13 observations
## mean=65.15385, MSE=12.89941
##
## Node number 32: 9 observations
## mean=14.33333, MSE=7.555556
##
## Node number 33: 31 observations, complexity param=0.0001609864
## mean=17.29032, MSE=6.141519
## left son=66 (17 obs) right son=67 (14 obs)
## Primary splits:
## DRB < 2358.5 to the left, improve=0.11447550, (0 missing)
## PTS.diff < -740.5 to the left, improve=0.04735753, (0 missing)
## Surrogate splits:
## PTS.diff < -744 to the left, agree=0.645, adj=0.214, (0 split)
##
## Node number 34: 20 observations, complexity param=6.753783e-05
## mean=19.65, MSE=8.7275
## left son=68 (13 obs) right son=69 (7 obs)
## Primary splits:
## PTS.diff < -600 to the left, improve=0.05238274, (0 missing)
## DRB < 2237.5 to the right, improve=0.02722441, (0 missing)
## Surrogate splits:
## DRB < 2278.5 to the left, agree=0.7, adj=0.143, (0 split)
##
## Node number 35: 7 observations
## mean=23.28571, MSE=0.7755102
##
## Node number 36: 25 observations, complexity param=5.518541e-05
## mean=22.84, MSE=9.4944
## left son=72 (9 obs) right son=73 (16 obs)
## Primary splits:
## PTS.diff < -519.5 to the right, improve=0.03147586, (0 missing)
## DRB < 2304.5 to the right, improve=0.03130868, (0 missing)
## Surrogate splits:
## DRB < 2561.5 to the right, agree=0.72, adj=0.222, (0 split)
##
## Node number 37: 19 observations
## mean=24.31579, MSE=7.689751
##
## Node number 38: 41 observations, complexity param=0.0002197229
## mean=26.39024, MSE=5.555027
## left son=76 (19 obs) right son=77 (22 obs)
## Primary splits:
## PTS.diff < -411 to the left, improve=0.1306069, (0 missing)
## DRB < 2435.5 to the right, improve=0.1226056, (0 missing)
## Surrogate splits:
## DRB < 2375.5 to the left, agree=0.61, adj=0.158, (0 split)
##
## Node number 39: 7 observations
## mean=28.85714, MSE=7.836735
##
## Node number 40: 7 observations
## mean=28, MSE=2.857143
##
## Node number 41: 47 observations, complexity param=0.0002603248
## mean=30.21277, MSE=10.42282
## left son=82 (38 obs) right son=83 (9 obs)
## Primary splits:
## PTS.diff < -293.5 to the left, improve=0.05565563, (0 missing)
## DRB < 2384 to the right, improve=0.02647364, (0 missing)
##
## Node number 42: 10 observations
## mean=31.5, MSE=2.65
##
## Node number 43: 13 observations
## mean=32.84615, MSE=9.514793
##
## Node number 44: 12 observations
## mean=33.58333, MSE=4.076389
##
## Node number 45: 49 observations, complexity param=0.0004905921
## mean=36.16327, MSE=8.871304
## left son=90 (38 obs) right son=91 (11 obs)
## Primary splits:
## DRB < 2392.5 to the right, improve=0.15798410, (0 missing)
## PTS.diff < -151 to the left, improve=0.01881491, (0 missing)
##
## Node number 46: 69 observations, complexity param=0.0004309939
## mean=38.13043, MSE=7.359798
## left son=92 (36 obs) right son=93 (33 obs)
## Primary splits:
## PTS.diff < -80.5 to the left, improve=0.1148992, (0 missing)
## DRB < 2431.5 to the left, improve=0.0224669, (0 missing)
## Surrogate splits:
## DRB < 2405 to the left, agree=0.667, adj=0.303, (0 split)
##
## Node number 47: 7 observations
## mean=41.28571, MSE=3.346939
##
## Node number 48: 48 observations, complexity param=0.0002836418
## mean=41.625, MSE=6.484375
## left son=96 (40 obs) right son=97 (8 obs)
## Primary splits:
## PTS.diff < -15 to the right, improve=0.12337350, (0 missing)
## DRB < 2375 to the right, improve=0.03000446, (0 missing)
##
## Node number 49: 49 observations, complexity param=0.0003697523
## mean=42.95918, MSE=5.304456
## left son=98 (21 obs) right son=99 (28 obs)
## Primary splits:
## PTS.diff < 1.5 to the left, improve=0.2191230, (0 missing)
## DRB < 2449.5 to the right, improve=0.1003487, (0 missing)
## Surrogate splits:
## DRB < 2470.5 to the right, agree=0.612, adj=0.095, (0 split)
##
## Node number 50: 12 observations
## mean=43, MSE=5
##
## Node number 51: 50 observations, complexity param=0.000301649
## mean=45.04, MSE=9.4384
## left son=102 (42 obs) right son=103 (8 obs)
## Primary splits:
## PTS.diff < 140 to the left, improve=0.08773127, (0 missing)
## DRB < 2390 to the left, improve=0.02113823, (0 missing)
##
## Node number 52: 60 observations, complexity param=0.0001650639
## mean=47.63333, MSE=7.298889
## left son=104 (19 obs) right son=105 (41 obs)
## Primary splits:
## DRB < 2474 to the right, improve=0.05102759, (0 missing)
## PTS.diff < 200.5 to the right, improve=0.02762978, (0 missing)
##
## Node number 53: 7 observations
## mean=49.57143, MSE=11.67347
##
## Node number 54: 22 observations, complexity param=0.0002350252
## mean=49.40909, MSE=8.605372
## left son=108 (14 obs) right son=109 (8 obs)
## Primary splits:
## PTS.diff < 275 to the right, improve=0.1680672, (0 missing)
## DRB < 2294 to the right, improve=0.0923821, (0 missing)
##
## Node number 55: 27 observations, complexity param=0.0005103544
## mean=51.88889, MSE=9.061728
## left son=110 (16 obs) right son=111 (11 obs)
## Primary splits:
## DRB < 2516.5 to the right, improve=0.28239570, (0 missing)
## PTS.diff < 286.5 to the left, improve=0.01117785, (0 missing)
## Surrogate splits:
## PTS.diff < 259.5 to the right, agree=0.667, adj=0.182, (0 split)
##
## Node number 58: 21 observations, complexity param=0.0001188876
## mean=52.90476, MSE=7.038549
## left son=116 (14 obs) right son=117 (7 obs)
## Primary splits:
## PTS.diff < 321.5 to the right, improve=0.10889180, (0 missing)
## DRB < 2461.5 to the right, improve=0.08521263, (0 missing)
##
## Node number 59: 54 observations, complexity param=0.0002123464
## mean=54.38889, MSE=5.867284
## left son=118 (45 obs) right son=119 (9 obs)
## Primary splits:
## PTS.diff < 356 to the right, improve=0.08847975, (0 missing)
## DRB < 2639 to the right, improve=0.04639663, (0 missing)
##
## Node number 60: 56 observations, complexity param=0.0002443703
## mean=58.5, MSE=8.892857
## left son=120 (7 obs) right son=121 (49 obs)
## Primary splits:
## PTS.diff < 557.5 to the right, improve=0.03614458, (0 missing)
## DRB < 2491.5 to the right, improve=0.03114963, (0 missing)
##
## Node number 61: 35 observations, complexity param=0.000200752
## mean=60, MSE=7.542857
## left son=122 (12 obs) right son=123 (23 obs)
## Primary splits:
## DRB < 2601.5 to the right, improve=0.09414800, (0 missing)
## PTS.diff < 629.5 to the right, improve=0.01668786, (0 missing)
## Surrogate splits:
## PTS.diff < 586 to the left, agree=0.686, adj=0.083, (0 split)
##
## Node number 66: 17 observations
## mean=16.52941, MSE=7.425606
##
## Node number 67: 14 observations
## mean=18.21429, MSE=3.02551
##
## Node number 68: 13 observations
## mean=19.15385, MSE=5.207101
##
## Node number 69: 7 observations
## mean=20.57143, MSE=13.95918
##
## Node number 72: 9 observations
## mean=22.11111, MSE=1.654321
##
## Node number 73: 16 observations
## mean=23.25, MSE=13.4375
##
## Node number 76: 19 observations
## mean=25.47368, MSE=4.880886
##
## Node number 77: 22 observations, complexity param=2.99777e-05
## mean=27.18182, MSE=4.785124
## left son=154 (14 obs) right son=155 (8 obs)
## Primary splits:
## DRB < 2340 to the right, improve=0.03855169, (0 missing)
## PTS.diff < -383 to the right, improve=0.02538860, (0 missing)
## Surrogate splits:
## PTS.diff < -378 to the right, agree=0.682, adj=0.125, (0 split)
##
## Node number 82: 38 observations, complexity param=0.0002603248
## mean=29.84211, MSE=9.238227
## left son=164 (10 obs) right son=165 (28 obs)
## Primary splits:
## DRB < 2275 to the left, improve=0.06963483, (0 missing)
## PTS.diff < -350.5 to the right, improve=0.02518326, (0 missing)
## Surrogate splits:
## PTS.diff < -296 to the right, agree=0.763, adj=0.1, (0 split)
##
## Node number 83: 9 observations
## mean=31.77778, MSE=12.39506
##
## Node number 90: 38 observations, complexity param=0.0001645247
## mean=35.52632, MSE=8.196676
## left son=180 (20 obs) right son=181 (18 obs)
## Primary splits:
## PTS.diff < -164.5 to the left, improve=0.07151065, (0 missing)
## DRB < 2474 to the left, improve=0.04964297, (0 missing)
## Surrogate splits:
## DRB < 2435 to the right, agree=0.632, adj=0.222, (0 split)
##
## Node number 91: 11 observations
## mean=38.36364, MSE=4.958678
##
## Node number 92: 36 observations, complexity param=0.0001348997
## mean=37.25, MSE=6.131944
## left son=184 (22 obs) right son=185 (14 obs)
## Primary splits:
## PTS.diff < -111.5 to the right, improve=0.08273154, (0 missing)
## DRB < 2335 to the right, improve=0.01634510, (0 missing)
## Surrogate splits:
## DRB < 2286.5 to the right, agree=0.667, adj=0.143, (0 split)
##
## Node number 93: 33 observations, complexity param=8.341044e-05
## mean=39.09091, MSE=6.931129
## left son=186 (25 obs) right son=187 (8 obs)
## Primary splits:
## PTS.diff < -70 to the right, improve=0.04937003, (0 missing)
## DRB < 2431.5 to the left, improve=0.02868857, (0 missing)
##
## Node number 96: 40 observations, complexity param=5.503109e-05
## mean=41.225, MSE=5.624375
## left son=192 (19 obs) right son=193 (21 obs)
## Primary splits:
## PTS.diff < 41 to the left, improve=0.030513280, (0 missing)
## DRB < 2369 to the right, improve=0.009797385, (0 missing)
## Surrogate splits:
## DRB < 2357.5 to the right, agree=0.6, adj=0.158, (0 split)
##
## Node number 97: 8 observations
## mean=43.625, MSE=5.984375
##
## Node number 98: 21 observations, complexity param=7.755832e-05
## mean=41.71429, MSE=3.918367
## left son=196 (14 obs) right son=197 (7 obs)
## Primary splits:
## PTS.diff < -24 to the right, improve=0.12760420, (0 missing)
## DRB < 2511.5 to the right, improve=0.08012821, (0 missing)
## Surrogate splits:
## DRB < 2559 to the left, agree=0.714, adj=0.143, (0 split)
##
## Node number 99: 28 observations, complexity param=0.0001625911
## mean=43.89286, MSE=4.309949
## left son=198 (21 obs) right son=199 (7 obs)
## Primary splits:
## DRB < 2446.5 to the right, improve=0.18240110, (0 missing)
## PTS.diff < 67.5 to the left, improve=0.06658775, (0 missing)
## Surrogate splits:
## PTS.diff < 12.5 to the right, agree=0.821, adj=0.286, (0 split)
##
## Node number 102: 42 observations, complexity param=0.0001142798
## mean=44.64286, MSE=7.610544
## left son=204 (35 obs) right son=205 (7 obs)
## Primary splits:
## DRB < 2517 to the left, improve=0.04840223, (0 missing)
## PTS.diff < 128 to the right, improve=0.03650279, (0 missing)
##
## Node number 103: 8 observations
## mean=47.125, MSE=13.85938
##
## Node number 104: 19 observations
## mean=46.73684, MSE=6.404432
##
## Node number 105: 41 observations, complexity param=0.0001180149
## mean=48.04878, MSE=7.168352
## left son=210 (19 obs) right son=211 (22 obs)
## Primary splits:
## PTS.diff < 200.5 to the right, improve=0.04747384, (0 missing)
## DRB < 2355.5 to the left, improve=0.04491725, (0 missing)
## Surrogate splits:
## DRB < 2283 to the left, agree=0.634, adj=0.211, (0 split)
##
## Node number 108: 14 observations
## mean=48.5, MSE=7.25
##
## Node number 109: 8 observations
## mean=51, MSE=7
##
## Node number 110: 16 observations
## mean=50.5625, MSE=8.246094
##
## Node number 111: 11 observations
## mean=53.81818, MSE=3.966942
##
## Node number 116: 14 observations
## mean=52.28571, MSE=6.061224
##
## Node number 117: 7 observations
## mean=54.14286, MSE=6.693878
##
## Node number 118: 45 observations, complexity param=0.0002123464
## mean=54.06667, MSE=5.662222
## left son=236 (17 obs) right son=237 (28 obs)
## Primary splits:
## DRB < 2576.5 to the right, improve=0.09657270, (0 missing)
## PTS.diff < 429.5 to the left, improve=0.08992793, (0 missing)
## Surrogate splits:
## PTS.diff < 447.5 to the right, agree=0.644, adj=0.059, (0 split)
##
## Node number 119: 9 observations
## mean=56, MSE=3.777778
##
## Node number 120: 7 observations
## mean=57, MSE=8.857143
##
## Node number 121: 49 observations, complexity param=0.0002443703
## mean=58.71429, MSE=8.530612
## left son=242 (42 obs) right son=243 (7 obs)
## Primary splits:
## PTS.diff < 542.5 to the left, improve=0.11523130, (0 missing)
## DRB < 2491.5 to the right, improve=0.07762948, (0 missing)
##
## Node number 122: 12 observations
## mean=58.83333, MSE=5.138889
##
## Node number 123: 23 observations, complexity param=0.000200752
## mean=60.6087, MSE=7.716446
## left son=246 (13 obs) right son=247 (10 obs)
## Primary splits:
## DRB < 2502 to the left, improve=0.16622510, (0 missing)
## PTS.diff < 629.5 to the right, improve=0.05006784, (0 missing)
## Surrogate splits:
## PTS.diff < 629.5 to the right, agree=0.609, adj=0.1, (0 split)
##
## Node number 154: 14 observations
## mean=26.85714, MSE=3.979592
##
## Node number 155: 8 observations
## mean=27.75, MSE=5.6875
##
## Node number 164: 10 observations
## mean=28.5, MSE=10.85
##
## Node number 165: 28 observations, complexity param=0.0002603248
## mean=30.32143, MSE=7.789541
## left son=330 (20 obs) right son=331 (8 obs)
## Primary splits:
## DRB < 2384 to the right, improve=0.27249060, (0 missing)
## PTS.diff < -339 to the right, improve=0.05589215, (0 missing)
##
## Node number 180: 20 observations, complexity param=8.889782e-05
## mean=34.8, MSE=6.06
## left son=360 (13 obs) right son=361 (7 obs)
## Primary splits:
## DRB < 2519 to the left, improve=0.09930004, (0 missing)
## PTS.diff < -203 to the left, improve=0.05012376, (0 missing)
## Surrogate splits:
## PTS.diff < -175.5 to the left, agree=0.7, adj=0.143, (0 split)
##
## Node number 181: 18 observations
## mean=36.33333, MSE=9.333333
##
## Node number 184: 22 observations, complexity param=4.316789e-05
## mean=36.68182, MSE=5.671488
## left son=368 (8 obs) right son=369 (14 obs)
## Primary splits:
## PTS.diff < -89.5 to the right, improve=0.046838410, (0 missing)
## DRB < 2371.5 to the left, improve=0.009484778, (0 missing)
##
## Node number 185: 14 observations
## mean=38.14286, MSE=5.55102
##
## Node number 186: 25 observations, complexity param=5.042522e-05
## mean=38.76, MSE=5.4624
## left son=372 (15 obs) right son=373 (10 obs)
## Primary splits:
## DRB < 2458.5 to the left, improve=0.04999024, (0 missing)
## PTS.diff < -54.5 to the left, improve=0.02559362, (0 missing)
## Surrogate splits:
## PTS.diff < -39.5 to the left, agree=0.68, adj=0.2, (0 split)
##
## Node number 187: 8 observations
## mean=40.125, MSE=10.10938
##
## Node number 192: 19 observations
## mean=40.78947, MSE=8.481994
##
## Node number 193: 21 observations, complexity param=5.503109e-05
## mean=41.61905, MSE=2.712018
## left son=386 (12 obs) right son=387 (9 obs)
## Primary splits:
## PTS.diff < 63 to the right, improve=0.14109530, (0 missing)
## DRB < 2372 to the right, improve=0.02048495, (0 missing)
## Surrogate splits:
## DRB < 2382 to the right, agree=0.619, adj=0.111, (0 split)
##
## Node number 196: 14 observations
## mean=41.21429, MSE=4.311224
##
## Node number 197: 7 observations
## mean=42.71429, MSE=1.632653
##
## Node number 198: 21 observations, complexity param=0.0001094386
## mean=43.38095, MSE=3.854875
## left son=396 (10 obs) right son=397 (11 obs)
## Primary splits:
## PTS.diff < 72.5 to the left, improve=0.1830214, (0 missing)
## DRB < 2493.5 to the right, improve=0.0342246, (0 missing)
## Surrogate splits:
## DRB < 2484.5 to the right, agree=0.667, adj=0.3, (0 split)
##
## Node number 199: 7 observations
## mean=45.42857, MSE=2.530612
##
## Node number 204: 35 observations, complexity param=6.660433e-05
## mean=44.37143, MSE=7.433469
## left son=408 (11 obs) right son=409 (24 obs)
## Primary splits:
## PTS.diff < 106 to the left, improve=0.02558420, (0 missing)
## DRB < 2327.5 to the right, improve=0.02126071, (0 missing)
## Surrogate splits:
## DRB < 2189 to the left, agree=0.714, adj=0.091, (0 split)
##
## Node number 205: 7 observations
## mean=46, MSE=6.285714
##
## Node number 210: 19 observations
## mean=47.42105, MSE=6.34903
##
## Node number 211: 22 observations, complexity param=0.0001180149
## mean=48.59091, MSE=7.241736
## left son=422 (10 obs) right son=423 (12 obs)
## Primary splits:
## PTS.diff < 179 to the left, improve=0.1129910, (0 missing)
## DRB < 2386.5 to the right, improve=0.0175844, (0 missing)
## Surrogate splits:
## DRB < 2418 to the right, agree=0.682, adj=0.3, (0 split)
##
## Node number 236: 17 observations
## mean=53.11765, MSE=5.633218
##
## Node number 237: 28 observations, complexity param=0.0002123464
## mean=54.64286, MSE=4.80102
## left son=474 (20 obs) right son=475 (8 obs)
## Primary splits:
## PTS.diff < 430 to the left, improve=0.24997340, (0 missing)
## DRB < 2530 to the left, improve=0.07970244, (0 missing)
##
## Node number 242: 42 observations, complexity param=0.0002167317
## mean=58.30952, MSE=7.975624
## left son=484 (26 obs) right son=485 (16 obs)
## Primary splits:
## DRB < 2491.5 to the right, improve=0.08759302, (0 missing)
## PTS.diff < 476.5 to the right, improve=0.02121113, (0 missing)
## Surrogate splits:
## PTS.diff < 462 to the right, agree=0.667, adj=0.125, (0 split)
##
## Node number 243: 7 observations
## mean=61.14286, MSE=4.979592
##
## Node number 246: 13 observations
## mean=59.61538, MSE=4.544379
##
## Node number 247: 10 observations
## mean=61.9, MSE=8.89
##
## Node number 330: 20 observations, complexity param=7.506639e-05
## mean=29.4, MSE=5.34
## left son=660 (7 obs) right son=661 (13 obs)
## Primary splits:
## DRB < 2434.5 to the left, improve=0.09515578, (0 missing)
## PTS.diff < -323 to the right, improve=0.01613368, (0 missing)
## Surrogate splits:
## PTS.diff < -326.5 to the right, agree=0.75, adj=0.286, (0 split)
##
## Node number 331: 8 observations
## mean=32.625, MSE=6.484375
##
## Node number 360: 13 observations
## mean=34.23077, MSE=4.792899
##
## Node number 361: 7 observations
## mean=35.85714, MSE=6.693878
##
## Node number 368: 8 observations
## mean=36, MSE=7.5
##
## Node number 369: 14 observations
## mean=37.07143, MSE=4.209184
##
## Node number 372: 15 observations
## mean=38.33333, MSE=3.555556
##
## Node number 373: 10 observations
## mean=39.4, MSE=7.64
##
## Node number 386: 12 observations
## mean=41.08333, MSE=3.076389
##
## Node number 387: 9 observations
## mean=42.33333, MSE=1.333333
##
## Node number 396: 10 observations
## mean=42.5, MSE=1.85
##
## Node number 397: 11 observations
## mean=44.18182, MSE=4.330579
##
## Node number 408: 11 observations
## mean=43.72727, MSE=2.561983
##
## Node number 409: 24 observations, complexity param=6.660433e-05
## mean=44.66667, MSE=9.388889
## left son=818 (9 obs) right son=819 (15 obs)
## Primary splits:
## PTS.diff < 128 to the right, improve=0.0504931, (0 missing)
## DRB < 2454 to the right, improve=0.0156250, (0 missing)
## Surrogate splits:
## DRB < 2217.5 to the left, agree=0.708, adj=0.222, (0 split)
##
## Node number 422: 10 observations
## mean=47.6, MSE=5.44
##
## Node number 423: 12 observations
## mean=49.41667, MSE=7.243056
##
## Node number 474: 20 observations, complexity param=3.510214e-05
## mean=53.95, MSE=3.9475
## left son=948 (7 obs) right son=949 (13 obs)
## Primary splits:
## PTS.diff < 406.5 to the right, improve=0.06019250, (0 missing)
## DRB < 2465 to the right, improve=0.03124108, (0 missing)
##
## Node number 475: 8 observations
## mean=56.375, MSE=2.734375
##
## Node number 484: 26 observations, complexity param=9.835073e-05
## mean=57.65385, MSE=8.610947
## left son=968 (11 obs) right son=969 (15 obs)
## Primary splits:
## PTS.diff < 488 to the left, improve=0.05947223, (0 missing)
## DRB < 2559 to the left, improve=0.02628414, (0 missing)
## Surrogate splits:
## DRB < 2642 to the right, agree=0.654, adj=0.182, (0 split)
##
## Node number 485: 16 observations
## mean=59.375, MSE=5.109375
##
## Node number 660: 7 observations
## mean=28.42857, MSE=3.387755
##
## Node number 661: 13 observations
## mean=29.92308, MSE=5.609467
##
## Node number 818: 9 observations
## mean=43.77778, MSE=11.50617
##
## Node number 819: 15 observations
## mean=45.2, MSE=7.36
##
## Node number 948: 7 observations
## mean=53.28571, MSE=4.204082
##
## Node number 949: 13 observations
## mean=54.30769, MSE=3.443787
##
## Node number 968: 11 observations
## mean=56.81818, MSE=10.69421
##
## Node number 969: 15 observations
## mean=58.26667, MSE=6.195556
##
## n= 835
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 835 1.353820e+05 41.00000
## 2) PTS.diff< -32.5 373 2.241283e+04 29.46649
## 4) PTS.diff< -359.5 159 3.671937e+03 22.25157
## 8) PTS.diff< -572 67 8.216418e+02 18.22388
## 16) PTS.diff< -670.5 40 3.193750e+02 16.62500
## 32) PTS.diff< -829.5 9 6.800000e+01 14.33333 *
## 33) PTS.diff>=-829.5 31 1.903871e+02 17.29032
## 66) DRB< 2358.5 17 1.262353e+02 16.52941 *
## 67) DRB>=2358.5 14 4.235714e+01 18.21429 *
## 17) PTS.diff>=-670.5 27 2.485185e+02 20.59259
## 34) DRB< 2351 20 1.745500e+02 19.65000
## 68) PTS.diff< -600 13 6.769231e+01 19.15385 *
## 69) PTS.diff>=-600 7 9.771429e+01 20.57143 *
## 35) DRB>=2351 7 5.428571e+00 23.28571 *
## 9) PTS.diff>=-572 92 9.718587e+02 25.18478
## 18) PTS.diff< -458.5 44 4.069773e+02 23.47727
## 36) PTS.diff< -498.5 25 2.373600e+02 22.84000
## 72) PTS.diff>=-519.5 9 1.488889e+01 22.11111 *
## 73) PTS.diff< -519.5 16 2.150000e+02 23.25000 *
## 37) PTS.diff>=-498.5 19 1.461053e+02 24.31579 *
## 19) PTS.diff>=-458.5 48 3.190000e+02 26.75000
## 38) DRB< 2500 41 2.277561e+02 26.39024
## 76) PTS.diff< -411 19 9.273684e+01 25.47368 *
## 77) PTS.diff>=-411 22 1.052727e+02 27.18182
## 154) DRB>=2340 14 5.571429e+01 26.85714 *
## 155) DRB< 2340 8 4.550000e+01 27.75000 *
## 39) DRB>=2500 7 5.485714e+01 28.85714 *
## 5) PTS.diff>=-359.5 214 4.314603e+03 34.82710
## 10) PTS.diff< -232.5 77 7.880779e+02 30.62338
## 20) PTS.diff< -258.5 54 5.397037e+02 29.92593
## 40) PTS.diff>=-275 7 2.000000e+01 28.00000 *
## 41) PTS.diff< -275 47 4.898723e+02 30.21277
## 82) PTS.diff< -293.5 38 3.510526e+02 29.84211
## 164) DRB< 2275 10 1.085000e+02 28.50000 *
## 165) DRB>=2275 28 2.181071e+02 30.32143
## 330) DRB>=2384 20 1.068000e+02 29.40000
## 660) DRB< 2434.5 7 2.371429e+01 28.42857 *
## 661) DRB>=2434.5 13 7.292308e+01 29.92308 *
## 331) DRB< 2384 8 5.187500e+01 32.62500 *
## 83) PTS.diff>=-293.5 9 1.115556e+02 31.77778 *
## 21) PTS.diff>=-258.5 23 1.604348e+02 32.26087
## 42) DRB>=2441 10 2.650000e+01 31.50000 *
## 43) DRB< 2441 13 1.236923e+02 32.84615 *
## 11) PTS.diff>=-232.5 137 1.401066e+03 37.18978
## 22) PTS.diff< -125 61 5.477705e+02 35.65574
## 44) DRB< 2333.5 12 4.891667e+01 33.58333 *
## 45) DRB>=2333.5 49 4.346939e+02 36.16327
## 90) DRB>=2392.5 38 3.114737e+02 35.52632
## 180) PTS.diff< -164.5 20 1.212000e+02 34.80000
## 360) DRB< 2519 13 6.230769e+01 34.23077 *
## 361) DRB>=2519 7 4.685714e+01 35.85714 *
## 181) PTS.diff>=-164.5 18 1.680000e+02 36.33333 *
## 91) DRB< 2392.5 11 5.454545e+01 38.36364 *
## 23) PTS.diff>=-125 76 5.945263e+02 38.42105
## 46) DRB>=2272.5 69 5.078261e+02 38.13043
## 92) PTS.diff< -80.5 36 2.207500e+02 37.25000
## 184) PTS.diff>=-111.5 22 1.247727e+02 36.68182
## 368) PTS.diff>=-89.5 8 6.000000e+01 36.00000 *
## 369) PTS.diff< -89.5 14 5.892857e+01 37.07143 *
## 185) PTS.diff< -111.5 14 7.771429e+01 38.14286 *
## 93) PTS.diff>=-80.5 33 2.287273e+02 39.09091
## 186) PTS.diff>=-70 25 1.365600e+02 38.76000
## 372) DRB< 2458.5 15 5.333333e+01 38.33333 *
## 373) DRB>=2458.5 10 7.640000e+01 39.40000 *
## 187) PTS.diff< -70 8 8.087500e+01 40.12500 *
## 47) DRB< 2272.5 7 2.342857e+01 41.28571 *
## 3) PTS.diff>=-32.5 462 2.329312e+04 50.31169
## 6) PTS.diff< 316 275 4.997105e+03 45.68727
## 12) PTS.diff< 160 159 1.394730e+03 43.21384
## 24) PTS.diff< 87.5 97 6.143299e+02 42.29897
## 48) DRB< 2418.5 48 3.112500e+02 41.62500
## 96) PTS.diff>=-15 40 2.249750e+02 41.22500
## 192) PTS.diff< 41 19 1.611579e+02 40.78947 *
## 193) PTS.diff>=41 21 5.695238e+01 41.61905
## 386) PTS.diff>=63 12 3.691667e+01 41.08333 *
## 387) PTS.diff< 63 9 1.200000e+01 42.33333 *
## 97) PTS.diff< -15 8 4.787500e+01 43.62500 *
## 49) DRB>=2418.5 49 2.599184e+02 42.95918
## 98) PTS.diff< 1.5 21 8.228571e+01 41.71429
## 196) PTS.diff>=-24 14 6.035714e+01 41.21429 *
## 197) PTS.diff< -24 7 1.142857e+01 42.71429 *
## 99) PTS.diff>=1.5 28 1.206786e+02 43.89286
## 198) DRB>=2446.5 21 8.095238e+01 43.38095
## 396) PTS.diff< 72.5 10 1.850000e+01 42.50000 *
## 397) PTS.diff>=72.5 11 4.763636e+01 44.18182 *
## 199) DRB< 2446.5 7 1.771429e+01 45.42857 *
## 25) PTS.diff>=87.5 62 5.721935e+02 44.64516
## 50) DRB>=2548 12 6.000000e+01 43.00000 *
## 51) DRB< 2548 50 4.719200e+02 45.04000
## 102) PTS.diff< 140 42 3.196429e+02 44.64286
## 204) DRB< 2517 35 2.601714e+02 44.37143
## 408) PTS.diff< 106 11 2.818182e+01 43.72727 *
## 409) PTS.diff>=106 24 2.253333e+02 44.66667
## 818) PTS.diff>=128 9 1.035556e+02 43.77778 *
## 819) PTS.diff< 128 15 1.104000e+02 45.20000 *
## 205) DRB>=2517 7 4.400000e+01 46.00000 *
## 103) PTS.diff>=140 8 1.108750e+02 47.12500 *
## 13) PTS.diff>=160 116 1.296302e+03 49.07759
## 26) PTS.diff< 250.5 67 5.431940e+02 47.83582
## 52) DRB< 2579.5 60 4.379333e+02 47.63333
## 104) DRB>=2474 19 1.216842e+02 46.73684 *
## 105) DRB< 2474 41 2.939024e+02 48.04878
## 210) PTS.diff>=200.5 19 1.206316e+02 47.42105 *
## 211) PTS.diff< 200.5 22 1.593182e+02 48.59091
## 422) PTS.diff< 179 10 5.440000e+01 47.60000 *
## 423) PTS.diff>=179 12 8.691667e+01 49.41667 *
## 53) DRB>=2579.5 7 8.171429e+01 49.57143 *
## 27) PTS.diff>=250.5 49 5.085306e+02 50.77551
## 54) DRB< 2455 22 1.893182e+02 49.40909
## 108) PTS.diff>=275 14 1.015000e+02 48.50000 *
## 109) PTS.diff< 275 8 5.600000e+01 51.00000 *
## 55) DRB>=2455 27 2.446667e+02 51.88889
## 110) DRB>=2516.5 16 1.319375e+02 50.56250 *
## 111) DRB< 2516.5 11 4.363636e+01 53.81818 *
## 7) PTS.diff>=316 187 3.766642e+03 57.11230
## 14) PTS.diff< 449.5 83 6.294699e+02 53.69880
## 28) DRB< 2358 8 7.287500e+01 51.12500 *
## 29) DRB>=2358 75 4.979467e+02 53.97333
## 58) PTS.diff< 345 21 1.478095e+02 52.90476
## 116) PTS.diff>=321.5 14 8.485714e+01 52.28571 *
## 117) PTS.diff< 321.5 7 4.685714e+01 54.14286 *
## 59) PTS.diff>=345 54 3.168333e+02 54.38889
## 118) PTS.diff>=356 45 2.548000e+02 54.06667
## 236) DRB>=2576.5 17 9.576471e+01 53.11765 *
## 237) DRB< 2576.5 28 1.344286e+02 54.64286
## 474) PTS.diff< 430 20 7.895000e+01 53.95000
## 948) PTS.diff>=406.5 7 2.942857e+01 53.28571 *
## 949) PTS.diff< 406.5 13 4.476923e+01 54.30769 *
## 475) PTS.diff>=430 8 2.187500e+01 56.37500 *
## 119) PTS.diff< 356 9 3.400000e+01 56.00000 *
## 15) PTS.diff>=449.5 104 1.398221e+03 59.83654
## 30) PTS.diff< 696 91 8.104615e+02 59.07692
## 60) PTS.diff< 582.5 56 4.980000e+02 58.50000
## 120) PTS.diff>=557.5 7 6.200000e+01 57.00000 *
## 121) PTS.diff< 557.5 49 4.180000e+02 58.71429
## 242) PTS.diff< 542.5 42 3.349762e+02 58.30952
## 484) DRB>=2491.5 26 2.238846e+02 57.65385
## 968) PTS.diff< 488 11 1.176364e+02 56.81818 *
## 969) PTS.diff>=488 15 9.293333e+01 58.26667 *
## 485) DRB< 2491.5 16 8.175000e+01 59.37500 *
## 243) PTS.diff>=542.5 7 3.485714e+01 61.14286 *
## 61) PTS.diff>=582.5 35 2.640000e+02 60.00000
## 122) DRB>=2601.5 12 6.166667e+01 58.83333 *
## 123) DRB< 2601.5 23 1.774783e+02 60.60870
## 246) DRB< 2502 13 5.907692e+01 59.61538 *
## 247) DRB>=2502 10 8.890000e+01 61.90000 *
## 31) PTS.diff>=696 13 1.676923e+02 65.15385 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.cv.0.cp.0.rpart rpart PTS.diff, DRB 0
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 0.469 0.018 0.9612337
## min.RMSE.fit max.R.sq.OOB min.RMSE.OOB
## 1 2.507057 0.9260059 3.494519if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.rpart"
## [1] " indep_vars: PTS.diff, DRB"## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.107 on full training set## Warning in myfit_mdl(model_id = "Max.cor.Y", model_method = "rpart",
## model_type = glb_model_type, : model's bestTune found at an extreme of
## tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 835
##
## CP nsplit rel error
## 1 0.6623927 0 1.0000000
## 2 0.1073213 1 0.3376073
## 3 0.1065599 2 0.2302860
##
## Variable importance
## PTS.diff DRB
## 80 20
##
## Node number 1: 835 observations, complexity param=0.6623927
## mean=41, MSE=162.1341
## left son=2 (373 obs) right son=3 (462 obs)
## Primary splits:
## PTS.diff < -32.5 to the left, improve=0.6623927, (0 missing)
## DRB < 2377.5 to the left, improve=0.1549703, (0 missing)
## Surrogate splits:
## DRB < 2377.5 to the left, agree=0.668, adj=0.257, (0 split)
##
## Node number 2: 373 observations
## mean=29.46649, MSE=60.08802
##
## Node number 3: 462 observations, complexity param=0.1073213
## mean=50.31169, MSE=50.418
## left son=6 (275 obs) right son=7 (187 obs)
## Primary splits:
## PTS.diff < 316 to the left, improve=0.62376240, (0 missing)
## DRB < 2582.5 to the left, improve=0.09916884, (0 missing)
## Surrogate splits:
## DRB < 2584 to the left, agree=0.688, adj=0.23, (0 split)
##
## Node number 6: 275 observations
## mean=45.68727, MSE=18.17129
##
## Node number 7: 187 observations
## mean=57.1123, MSE=20.14247
##
## n= 835
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 835 135382.000 41.00000
## 2) PTS.diff< -32.5 373 22412.830 29.46649 *
## 3) PTS.diff>=-32.5 462 23293.120 50.31169
## 6) PTS.diff< 316 275 4997.105 45.68727 *
## 7) PTS.diff>=316 187 3766.642 57.11230 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.rpart rpart PTS.diff, DRB 3
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 1.032 0.018 0.769714
## min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit min.RMSESD.fit
## 1 5.970313 0.8688472 4.652407 0.7716525 1.571127
## max.RsquaredSD.fit
## 1 0.119624# Used to compare vs. Interactions.High.cor.Y and/or Max.cor.Y.TmSrs
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.lm"
## [1] " indep_vars: PTS.diff, DRB"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.3744 -2.0916 -0.1428 2.0214 10.5207
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.579e+01 2.224e+00 16.095 <2e-16 ***
## PTS.diff 3.224e-02 3.151e-04 102.335 <2e-16 ***
## DRB 2.145e-03 9.151e-04 2.344 0.0193 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.053 on 832 degrees of freedom
## Multiple R-squared: 0.9427, Adjusted R-squared: 0.9426
## F-statistic: 6847 on 2 and 832 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.lm lm PTS.diff, DRB 1
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 0.932 0.003 0.9427209
## min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit max.Rsquared.fit
## 1 3.067261 0.9401179 3.143675 0.9425832 0.9432758
## min.RMSESD.fit max.RsquaredSD.fit
## 1 0.1709692 0.005666001if (!is.null(glb_date_vars) &&
(sum(grepl(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df))) > 0)) {
# ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly1",
# model_method=ifelse(glb_is_regression, "lm",
# ifelse(glb_is_binomial, "glm", "rpart")),
# model_type=glb_model_type,
# indep_vars_vctr=c(max_cor_y_x_vars, paste0(glb_date_vars, ".day.minutes")),
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
#
ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=c(max_cor_y_x_vars,
grep(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df), value=TRUE)),
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(unique(glb_feats_df$cor.high.X), NA)) > 0) {
# lm & glm handle interaction terms; rpart & rf do not
if (glb_is_regression || glb_is_binomial) {
indep_vars_vctr <-
c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":"))
} else { indep_vars_vctr <- union(max_cor_y_x_vars, int_feats) }
ret_lst <- myfit_mdl(model_id="Interact.High.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
} ## [1] "fitting model: Interact.High.cor.Y.lm"
## [1] " indep_vars: PTS.diff, DRB, PTS.diff:oppPTS, PTS.diff:PTS, PTS.diff:FT, PTS.diff:X3P, PTS.diff:X2PA"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.3505 -2.0452 -0.1462 2.0161 9.1299
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.608e+01 2.228e+00 16.190 < 2e-16 ***
## PTS.diff 4.806e-02 6.703e-03 7.169 1.67e-12 ***
## DRB 2.007e-03 9.163e-04 2.190 0.0288 *
## `PTS.diff:oppPTS` -2.819e-07 8.685e-07 -0.325 0.7456
## `PTS.diff:PTS` 1.669e-06 1.180e-06 1.414 0.1578
## `PTS.diff:FT` -4.279e-06 2.589e-06 -1.653 0.0988 .
## `PTS.diff:X3P` -1.031e-05 5.895e-06 -1.748 0.0808 .
## `PTS.diff:X2PA` -2.865e-06 1.771e-06 -1.617 0.1062
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.049 on 827 degrees of freedom
## Multiple R-squared: 0.9432, Adjusted R-squared: 0.9427
## F-statistic: 1962 on 7 and 827 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 Interact.High.cor.Y.lm lm
## feats
## 1 PTS.diff, DRB, PTS.diff:oppPTS, PTS.diff:PTS, PTS.diff:FT, PTS.diff:X3P, PTS.diff:X2PA
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 0.886 0.005
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.9431984 3.083847 0.943418 3.055823 0.9427176
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.9425587 0.1530238 0.00491491# Low.cor.X
# if (glb_is_classification && glb_is_binomial)
# indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) &
# is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"] else
indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) & !myNearZV &
(exclude.as.feat != 1))[, "id"]
myadjust_interaction_feats <- function(vars_vctr) {
for (feat in subset(glb_feats_df, !is.na(interaction.feat))$id)
if (feat %in% vars_vctr)
vars_vctr <- union(setdiff(vars_vctr, feat),
paste0(glb_feats_df[glb_feats_df$id == feat, "interaction.feat"], ":", feat))
return(vars_vctr)
}
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
ret_lst <- myfit_mdl(model_id="Low.cor.X",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Low.cor.X.lm"
## [1] " indep_vars: PTS.diff, DRB, AST, FT, BLK, X3P, STL, .rnorm, SeasonEnd, ORB, TOV, oppPTS"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.0100 -1.9832 -0.1585 1.9600 10.5287
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.163e+02 5.961e+01 1.951 0.05137 .
## PTS.diff 3.142e-02 6.122e-04 51.327 < 2e-16 ***
## DRB 1.985e-03 1.181e-03 1.681 0.09316 .
## AST 1.113e-03 9.103e-04 1.223 0.22172
## FT 2.762e-04 8.614e-04 0.321 0.74851
## BLK 3.958e-03 1.438e-03 2.753 0.00603 **
## X3P 1.060e-03 1.119e-03 0.948 0.34364
## STL 1.546e-04 1.600e-03 0.097 0.92304
## .rnorm 1.807e-01 1.087e-01 1.663 0.09667 .
## SeasonEnd -3.960e-02 2.956e-02 -1.340 0.18071
## ORB -6.175e-04 1.101e-03 -0.561 0.57503
## TOV -1.682e-03 1.216e-03 -1.384 0.16684
## oppPTS -3.503e-04 4.010e-04 -0.874 0.38258
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.044 on 822 degrees of freedom
## Multiple R-squared: 0.9437, Adjusted R-squared: 0.9429
## F-statistic: 1149 on 12 and 822 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 Low.cor.X.lm lm
## feats
## 1 PTS.diff, DRB, AST, FT, BLK, X3P, STL, .rnorm, SeasonEnd, ORB, TOV, oppPTS
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 0.915 0.006
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.9437381 3.119017 0.9412438 3.113979 0.9429167
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.9414636 0.2222216 0.007492174rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 10 fit.models 7 0 25.441 43.976 18.535
## 11 fit.models 7 1 43.977 NA NAfit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn")## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 48.244 NA NA# Options:
# 1. rpart & rf manual tuning
# 2. rf without pca (default: with pca)
#stop(here); sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df
#glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df
# All X that is not user excluded
# if (glb_is_classification && glb_is_binomial) {
# model_id_pfx <- "Conditional.X"
# # indep_vars_vctr <- setdiff(names(glb_fitobs_df), union(glb_rsp_var, glb_exclude_vars_as_features))
# indep_vars_vctr <- subset(glb_feats_df, is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"]
# } else {
model_id_pfx <- "All.X"
indep_vars_vctr <- subset(glb_feats_df, !myNearZV &
(exclude.as.feat != 1))[, "id"]
# }
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
for (method in glb_models_method_vctr) {
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", method), major.inc=TRUE)
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indep_vars_vctr <- setdiff(indep_vars_vctr, c(".rnorm"))
model_id <- paste0(model_id_pfx, ".no.rnorm")
} else model_id <- model_id_pfx
ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# If All.X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indep_vars_vctr
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(model_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glb_fitobs_df[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE), ]
# all.equal(glb_allobs_df$S.nuppr.log, glb_allobs_df$A.nuppr.log)
# all.equal(glb_allobs_df$S.npnct19.log, glb_allobs_df$A.npnct19.log)
# all.equal(glb_allobs_df$S.P.year.colon, glb_allobs_df$A.P.year.colon)
# all.equal(glb_allobs_df$S.T.share, glb_allobs_df$A.T.share)
# all.equal(glb_allobs_df$H.T.clip, glb_allobs_df$H.P.daily.clip.report)
# cor(glb_allobs_df$S.T.herald, glb_allobs_df$S.T.tribun)
# dsp_obs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# dsp_obs(Abstract.contains="[Ss]hare", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glb_allobs_df[, setdiff(names(glb_allobs_df), myfind_chr_cols_df(glb_allobs_df))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ".cp.0"), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
}## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 48.244 48.259 0.015
## 2 fit.models_1_lm 2 0 48.260 NA NA
## [1] "fitting model: All.X.lm"
## [1] " indep_vars: PTS.diff, DRB, AST, PTS, FT, BLK, FG, FTA, X3P, STL, X3PA, X2P, .rnorm, SeasonEnd, FGA, X2PA, ORB, TOV, oppPTS"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.7862 -1.9322 -0.1506 1.9776 10.6742
##
## Coefficients: (4 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.709e+02 6.247e+01 2.736 0.00635 **
## PTS.diff 3.066e-02 7.107e-04 43.140 < 2e-16 ***
## DRB 3.908e-03 1.538e-03 2.541 0.01124 *
## AST 8.808e-04 9.125e-04 0.965 0.33470
## PTS -8.300e-03 6.018e-03 -1.379 0.16818
## FT 9.264e-03 6.395e-03 1.449 0.14782
## BLK 3.865e-03 1.446e-03 2.673 0.00767 **
## FG 1.983e-02 1.231e-02 1.611 0.10754
## FTA -1.133e-03 1.694e-03 -0.669 0.50359
## X3P NA NA NA NA
## STL 2.173e-03 1.986e-03 1.094 0.27417
## X3PA 3.916e-03 2.345e-03 1.669 0.09541 .
## X2P NA NA NA NA
## .rnorm 2.000e-01 1.086e-01 1.841 0.06595 .
## SeasonEnd -6.528e-02 3.083e-02 -2.117 0.03456 *
## FGA -3.152e-03 1.295e-03 -2.435 0.01512 *
## X2PA NA NA NA NA
## ORB 2.030e-03 1.733e-03 1.171 0.24182
## TOV -3.645e-03 1.568e-03 -2.325 0.02031 *
## oppPTS NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.034 on 819 degrees of freedom
## Multiple R-squared: 0.9443, Adjusted R-squared: 0.9433
## F-statistic: 925.6 on 15 and 819 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
## model_id model_method
## 1 All.X.lm lm
## feats
## 1 PTS.diff, DRB, AST, PTS, FT, BLK, FG, FTA, X3P, STL, X3PA, X2P, .rnorm, SeasonEnd, FGA, X2PA, ORB, TOV, oppPTS
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 0.919 0.009
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.9442961 3.115887 0.9382505 3.192316 0.9432758
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.9415448 0.2207614 0.007401005
## label step_major step_minor bgn end elapsed
## 2 fit.models_1_lm 2 0 48.26 51.169 2.91
## 3 fit.models_1_glm 3 0 51.17 NA NA
## [1] "fitting model: All.X.glm"
## [1] " indep_vars: PTS.diff, DRB, AST, PTS, FT, BLK, FG, FTA, X3P, STL, X3PA, X2P, .rnorm, SeasonEnd, FGA, X2PA, ORB, TOV, oppPTS"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -8.7862 -1.9322 -0.1506 1.9776 10.6742
##
## Coefficients: (4 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.709e+02 6.247e+01 2.736 0.00635 **
## PTS.diff 3.066e-02 7.107e-04 43.140 < 2e-16 ***
## DRB 3.908e-03 1.538e-03 2.541 0.01124 *
## AST 8.808e-04 9.125e-04 0.965 0.33470
## PTS -8.300e-03 6.018e-03 -1.379 0.16818
## FT 9.264e-03 6.395e-03 1.449 0.14782
## BLK 3.865e-03 1.446e-03 2.673 0.00767 **
## FG 1.983e-02 1.231e-02 1.611 0.10754
## FTA -1.133e-03 1.694e-03 -0.669 0.50359
## X3P NA NA NA NA
## STL 2.173e-03 1.986e-03 1.094 0.27417
## X3PA 3.916e-03 2.345e-03 1.669 0.09541 .
## X2P NA NA NA NA
## .rnorm 2.000e-01 1.086e-01 1.841 0.06595 .
## SeasonEnd -6.528e-02 3.083e-02 -2.117 0.03456 *
## FGA -3.152e-03 1.295e-03 -2.435 0.01512 *
## X2PA NA NA NA NA
## ORB 2.030e-03 1.733e-03 1.171 0.24182
## TOV -3.645e-03 1.568e-03 -2.325 0.02031 *
## oppPTS NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 9.207951)
##
## Null deviance: 135382.0 on 834 degrees of freedom
## Residual deviance: 7541.3 on 819 degrees of freedom
## AIC: 4241.2
##
## Number of Fisher Scoring iterations: 2
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## model_id model_method
## 1 All.X.glm glm
## feats
## 1 PTS.diff, DRB, AST, PTS, FT, BLK, FG, FTA, X3P, STL, X3PA, X2P, .rnorm, SeasonEnd, FGA, X2PA, ORB, TOV, oppPTS
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 0.924 0.059
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB min.aic.fit
## 1 0.9442961 3.115887 0.9382505 3.192316 4241.228
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.9415448 0.2207614 0.007401005
## label step_major step_minor bgn end elapsed
## 3 fit.models_1_glm 3 0 51.170 54.008 2.838
## 4 fit.models_1_bayesglm 4 0 54.008 NA NA
## [1] "fitting model: All.X.bayesglm"
## [1] " indep_vars: PTS.diff, DRB, AST, PTS, FT, BLK, FG, FTA, X3P, STL, X3PA, X2P, .rnorm, SeasonEnd, FGA, X2PA, ORB, TOV, oppPTS"## Loading required package: arm
## Loading required package: MASS
##
## Attaching package: 'MASS'
##
## The following object is masked from 'package:dplyr':
##
## select
##
## Loading required package: Matrix
## Loading required package: lme4
## Loading required package: Rcpp
##
## arm (Version 1.8-5, built: 2015-05-13)
##
## Working directory is /Users/bbalaji-2012/Documents/Work/Courses/MIT/Analytics_Edge_15_071x/Recitations/Unit2_NBA## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -8.7862 -1.9322 -0.1506 1.9776 10.6743
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.709e+02 6.262e+01 2.729 0.00648 **
## PTS.diff 1.953e-02 2.972e+01 0.001 0.99948
## DRB 3.908e-03 1.542e-03 2.535 0.01144 *
## AST 8.808e-04 9.147e-04 0.963 0.33588
## PTS 8.666e-03 3.222e+01 0.000 0.99979
## FT 3.426e-03 2.115e+01 0.000 0.99987
## BLK 3.865e-03 1.450e-03 2.666 0.00782 **
## FG 3.814e-03 4.789e+01 0.000 0.99994
## FTA -1.133e-03 1.698e-03 -0.668 0.50463
## X3P -1.495e-03 4.074e+01 0.000 0.99997
## STL 2.173e-03 1.991e-03 1.092 0.27535
## X3PA 1.560e-03 2.856e+01 0.000 0.99996
## X2P 4.343e-03 3.124e+01 0.000 0.99989
## .rnorm 2.000e-01 1.089e-01 1.837 0.06662 .
## SeasonEnd -6.528e-02 3.091e-02 -2.112 0.03501 *
## FGA -7.962e-04 2.856e+01 0.000 0.99998
## X2PA -2.356e-03 2.856e+01 0.000 0.99993
## ORB 2.030e-03 1.737e-03 1.168 0.24297
## TOV -3.645e-03 1.571e-03 -2.319 0.02062 *
## oppPTS -1.113e-02 2.972e+01 0.000 0.99970
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 9.253144)
##
## Null deviance: 135382.0 on 834 degrees of freedom
## Residual deviance: 7541.3 on 815 degrees of freedom
## AIC: 4249.2
##
## Number of Fisher Scoring iterations: 14
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.X.bayesglm bayesglm
## feats
## 1 PTS.diff, DRB, AST, PTS, FT, BLK, FG, FTA, X3P, STL, X3PA, X2P, .rnorm, SeasonEnd, FGA, X2PA, ORB, TOV, oppPTS
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 4.557 0.113
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB min.aic.fit
## 1 0.9442961 3.115887 0.9382505 3.192316 4249.228
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.9415448 0.2207615 0.007401005
## label step_major step_minor bgn end elapsed
## 4 fit.models_1_bayesglm 4 0 54.008 59.504 5.496
## 5 fit.models_1_rpart 5 0 59.505 NA NA
## [1] "fitting model: All.X.no.rnorm.rpart"
## [1] " indep_vars: PTS.diff, DRB, AST, PTS, FT, BLK, FG, FTA, X3P, STL, X3PA, X2P, SeasonEnd, FGA, X2PA, ORB, TOV, oppPTS"## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.107 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 835
##
## CP nsplit rel error
## 1 0.6623927 0 1.0000000
## 2 0.1073213 1 0.3376073
## 3 0.1065599 2 0.2302860
##
## Variable importance
## PTS.diff DRB AST oppPTS PTS STL X3P
## 56 14 10 7 6 6 1
##
## Node number 1: 835 observations, complexity param=0.6623927
## mean=41, MSE=162.1341
## left son=2 (373 obs) right son=3 (462 obs)
## Primary splits:
## PTS.diff < -32.5 to the left, improve=0.66239270, (0 missing)
## DRB < 2377.5 to the left, improve=0.15497030, (0 missing)
## oppPTS < 7961 to the right, improve=0.12078130, (0 missing)
## AST < 2012.5 to the left, improve=0.07994841, (0 missing)
## TOV < 1145.5 to the right, improve=0.06589163, (0 missing)
## Surrogate splits:
## DRB < 2377.5 to the left, agree=0.668, adj=0.257, (0 split)
## AST < 1812 to the left, agree=0.641, adj=0.196, (0 split)
## oppPTS < 8026 to the right, agree=0.614, adj=0.137, (0 split)
## PTS < 7868 to the left, agree=0.613, adj=0.134, (0 split)
## STL < 651.5 to the left, agree=0.611, adj=0.129, (0 split)
##
## Node number 2: 373 observations
## mean=29.46649, MSE=60.08802
##
## Node number 3: 462 observations, complexity param=0.1073213
## mean=50.31169, MSE=50.418
## left son=6 (275 obs) right son=7 (187 obs)
## Primary splits:
## PTS.diff < 316 to the left, improve=0.62376240, (0 missing)
## DRB < 2582.5 to the left, improve=0.09916884, (0 missing)
## BLK < 403.5 to the left, improve=0.04759291, (0 missing)
## oppPTS < 9180 to the right, improve=0.03923016, (0 missing)
## AST < 2362 to the left, improve=0.02832203, (0 missing)
## Surrogate splits:
## DRB < 2584 to the left, agree=0.688, adj=0.230, (0 split)
## oppPTS < 7557.5 to the right, agree=0.630, adj=0.086, (0 split)
## X3P < 578 to the left, agree=0.626, adj=0.075, (0 split)
## AST < 2276 to the left, agree=0.613, adj=0.043, (0 split)
## X3PA < 1667 to the left, agree=0.613, adj=0.043, (0 split)
##
## Node number 6: 275 observations
## mean=45.68727, MSE=18.17129
##
## Node number 7: 187 observations
## mean=57.1123, MSE=20.14247
##
## n= 835
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 835 135382.000 41.00000
## 2) PTS.diff< -32.5 373 22412.830 29.46649 *
## 3) PTS.diff>=-32.5 462 23293.120 50.31169
## 6) PTS.diff< 316 275 4997.105 45.68727 *
## 7) PTS.diff>=316 187 3766.642 57.11230 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.X.no.rnorm.rpart rpart
## feats
## 1 PTS.diff, DRB, AST, PTS, FT, BLK, FG, FTA, X3P, STL, X3PA, X2P, SeasonEnd, FGA, X2PA, ORB, TOV, oppPTS
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 1.157 0.063
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.769714 5.970313 0.8688472 4.652407 0.7716525
## min.RMSESD.fit max.RsquaredSD.fit
## 1 1.571127 0.119624
## label step_major step_minor bgn end elapsed
## 5 fit.models_1_rpart 5 0 59.505 62.683 3.178
## 6 fit.models_1_rf 6 0 62.684 NA NA
## [1] "fitting model: All.X.no.rnorm.rf"
## [1] " indep_vars: PTS.diff, DRB, AST, PTS, FT, BLK, FG, FTA, X3P, STL, X3PA, X2P, SeasonEnd, FGA, X2PA, ORB, TOV, oppPTS"## Loading required package: randomForest
## randomForest 4.6-10
## Type rfNews() to see new features/changes/bug fixes.
##
## Attaching package: 'randomForest'
##
## The following object is masked from 'package:dplyr':
##
## combine
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 18 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: mtry
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 835 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 835 -none- numeric
## importance 18 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 835 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 18 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.X.no.rnorm.rf rf
## feats
## 1 PTS.diff, DRB, AST, PTS, FT, BLK, FG, FTA, X3P, STL, X3PA, X2P, SeasonEnd, FGA, X2PA, ORB, TOV, oppPTS
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 16.812 6.345
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.9899911 3.134206 0.9294243 3.412877 0.9398484
## min.RMSESD.fit max.RsquaredSD.fit
## 1 0.1565449 0.005243458# User specified
# Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
# easier to exclude features
#model_id_pfx <- "";
# indep_vars_vctr <- setdiff(names(glb_fitobs_df),
# union(union(glb_rsp_var, glb_exclude_vars_as_features),
# c("<feat1_name>", "<feat2_name>")))
# method <- ""
# easier to include features
model_id <- "PTS.only"; indep_vars_vctr <- c(NULL
,"PTS", "oppPTS"
# ,"<feat1>*<feat2>"
# ,"<feat1>:<feat2>"
)
for (method in c("lm")) {
ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
csm_mdl_id <- paste0(model_id, ".", method)
csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(model_id, ".", method)]]); print(head(csm_featsimp_df))
}## [1] "fitting model: PTS.only.lm"
## [1] " indep_vars: PTS, oppPTS"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.7321 -2.1093 -0.0583 2.0138 10.6157
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 41.3048239 1.6101879 25.65 <2e-16 ***
## PTS 0.0325672 0.0002971 109.60 <2e-16 ***
## oppPTS -0.0326036 0.0002939 -110.95 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.063 on 832 degrees of freedom
## Multiple R-squared: 0.9423, Adjusted R-squared: 0.9422
## F-statistic: 6799 on 2 and 832 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats max.nTuningRuns
## 1 PTS.only.lm lm PTS, oppPTS 1
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 0.858 0.003 0.942345
## min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit max.Rsquared.fit
## 1 3.075688 0.9417946 3.099349 0.9422064 0.9429442
## min.RMSESD.fit max.RsquaredSD.fit
## 1 0.1709393 0.005805662
## importance
## oppPTS 100
## PTS 0model_id <- "PTS.interact"; indep_vars_vctr <- c("oppPTS", "oppPTS*PTS")
for (method in c("lm")) {
ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
csm_mdl_id <- paste0(model_id, ".", method)
csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(model_id, ".", method)]]); print(head(csm_featsimp_df))
}## [1] "fitting model: PTS.interact.lm"
## [1] " indep_vars: oppPTS, oppPTS*PTS"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.6462 -2.1099 -0.0962 2.0463 10.7746
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.450e+01 1.898e+01 3.398 0.00071 ***
## oppPTS -3.534e-02 2.246e-03 -15.730 < 2e-16 ***
## PTS 2.979e-02 2.282e-03 13.056 < 2e-16 ***
## `oppPTS:PTS` 3.256e-07 2.654e-07 1.227 0.22028
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.062 on 831 degrees of freedom
## Multiple R-squared: 0.9424, Adjusted R-squared: 0.9422
## F-statistic: 4536 on 3 and 831 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats max.nTuningRuns
## 1 PTS.interact.lm lm oppPTS, oppPTS*PTS 1
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 0.851 0.003 0.9424492
## min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit max.Rsquared.fit
## 1 3.085891 0.9421886 3.088843 0.9422414 0.942569
## min.RMSESD.fit max.RsquaredSD.fit
## 1 0.1746107 0.005991684
## importance
## oppPTS 100.00000
## PTS 81.56245
## `oppPTS:PTS` 0.00000# model_id <- "W.only.no.<rsp_var>.fctr"; indep_vars_vctr <- c(NULL
# ,"W"
# # ,"<feat1>*<feat2>"
# # ,"<feat1>:<feat2>"
# )
# for (method in c("lm")) {
# ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var="<rsp_var>", rsp_var_out="<rsp_var>.predict.",
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# csm_mdl_id <- paste0(model_id, ".", method)
# csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(model_id, ".", method)]]); print(head(csm_featsimp_df))
# }
#print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, dsp_models_cols])
#csm_featsimp_df[grepl("H.npnct19.log", row.names(csm_featsimp_df)), , FALSE]
#csm_OOBobs_df <- glb_get_predictions(glb_OOBobs_df, mdl_id=csm_mdl_id, rsp_var_out=glb_rsp_var_out, prob_threshold_def=glb_models_df[glb_models_df$model_id == csm_mdl_id, "opt.prob.threshold.OOB"])
#print(sprintf("%s OOB confusion matrix & accuracy: ", csm_mdl_id)); print(t(confusionMatrix(csm_OOBobs_df[, paste0(glb_rsp_var_out, csm_mdl_id)], csm_OOBobs_df[, glb_rsp_var])$table))
#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$importance)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$importance)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]
#print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id)); print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)], glb_OOBobs_df[, glb_rsp_var])$table))
# User specified bivariate models
# indep_vars_vctr_lst <- list()
# for (feat in setdiff(names(glb_fitobs_df),
# union(glb_rsp_var, glb_exclude_vars_as_features)))
# indep_vars_vctr_lst[["feat"]] <- feat
# User specified combinatorial models
# indep_vars_vctr_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(model_id=paste0(model_id_pfx, ""), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df,
# model_loss_mtrx=glb_model_metric_terms,
# model_summaryFunction=glb_model_metric_smmry,
# model_metric=glb_model_metric,
# model_metric_maximize=glb_model_metric_maximize)
# Simplify a model
# fit_df <- glb_fitobs_df; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glb_fitobs_df, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glb_model_metric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## model_id model_method
## MFO.lm MFO.lm lm
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart Max.cor.Y.rpart rpart
## Max.cor.Y.lm Max.cor.Y.lm lm
## Interact.High.cor.Y.lm Interact.High.cor.Y.lm lm
## Low.cor.X.lm Low.cor.X.lm lm
## All.X.lm All.X.lm lm
## All.X.glm All.X.glm glm
## All.X.bayesglm All.X.bayesglm bayesglm
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart rpart
## All.X.no.rnorm.rf All.X.no.rnorm.rf rf
## PTS.only.lm PTS.only.lm lm
## PTS.interact.lm PTS.interact.lm lm
## feats
## MFO.lm .rnorm
## Max.cor.Y.cv.0.rpart PTS.diff, DRB
## Max.cor.Y.cv.0.cp.0.rpart PTS.diff, DRB
## Max.cor.Y.rpart PTS.diff, DRB
## Max.cor.Y.lm PTS.diff, DRB
## Interact.High.cor.Y.lm PTS.diff, DRB, PTS.diff:oppPTS, PTS.diff:PTS, PTS.diff:FT, PTS.diff:X3P, PTS.diff:X2PA
## Low.cor.X.lm PTS.diff, DRB, AST, FT, BLK, X3P, STL, .rnorm, SeasonEnd, ORB, TOV, oppPTS
## All.X.lm PTS.diff, DRB, AST, PTS, FT, BLK, FG, FTA, X3P, STL, X3PA, X2P, .rnorm, SeasonEnd, FGA, X2PA, ORB, TOV, oppPTS
## All.X.glm PTS.diff, DRB, AST, PTS, FT, BLK, FG, FTA, X3P, STL, X3PA, X2P, .rnorm, SeasonEnd, FGA, X2PA, ORB, TOV, oppPTS
## All.X.bayesglm PTS.diff, DRB, AST, PTS, FT, BLK, FG, FTA, X3P, STL, X3PA, X2P, .rnorm, SeasonEnd, FGA, X2PA, ORB, TOV, oppPTS
## All.X.no.rnorm.rpart PTS.diff, DRB, AST, PTS, FT, BLK, FG, FTA, X3P, STL, X3PA, X2P, SeasonEnd, FGA, X2PA, ORB, TOV, oppPTS
## All.X.no.rnorm.rf PTS.diff, DRB, AST, PTS, FT, BLK, FG, FTA, X3P, STL, X3PA, X2P, SeasonEnd, FGA, X2PA, ORB, TOV, oppPTS
## PTS.only.lm PTS, oppPTS
## PTS.interact.lm oppPTS, oppPTS*PTS
## max.nTuningRuns min.elapsedtime.everything
## MFO.lm 0 0.450
## Max.cor.Y.cv.0.rpart 0 0.603
## Max.cor.Y.cv.0.cp.0.rpart 0 0.469
## Max.cor.Y.rpart 3 1.032
## Max.cor.Y.lm 1 0.932
## Interact.High.cor.Y.lm 1 0.886
## Low.cor.X.lm 1 0.915
## All.X.lm 1 0.919
## All.X.glm 1 0.924
## All.X.bayesglm 1 4.557
## All.X.no.rnorm.rpart 3 1.157
## All.X.no.rnorm.rf 3 16.812
## PTS.only.lm 1 0.858
## PTS.interact.lm 1 0.851
## min.elapsedtime.final max.R.sq.fit min.RMSE.fit
## MFO.lm 0.003 3.833404e-05 12.732946
## Max.cor.Y.cv.0.rpart 0.019 0.000000e+00 12.733190
## Max.cor.Y.cv.0.cp.0.rpart 0.018 9.612337e-01 2.507057
## Max.cor.Y.rpart 0.018 7.697140e-01 5.970313
## Max.cor.Y.lm 0.003 9.427209e-01 3.067261
## Interact.High.cor.Y.lm 0.005 9.431984e-01 3.083847
## Low.cor.X.lm 0.006 9.437381e-01 3.119017
## All.X.lm 0.009 9.442961e-01 3.115887
## All.X.glm 0.059 9.442961e-01 3.115887
## All.X.bayesglm 0.113 9.442961e-01 3.115887
## All.X.no.rnorm.rpart 0.063 7.697140e-01 5.970313
## All.X.no.rnorm.rf 6.345 9.899911e-01 3.134206
## PTS.only.lm 0.003 9.423450e-01 3.075688
## PTS.interact.lm 0.003 9.424492e-01 3.085891
## max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## MFO.lm 0.0002542561 12.844989 -0.0011621
## Max.cor.Y.cv.0.rpart 0.0000000000 12.846623 NA
## Max.cor.Y.cv.0.cp.0.rpart 0.9260059366 3.494519 NA
## Max.cor.Y.rpart 0.8688472184 4.652407 NA
## Max.cor.Y.lm 0.9401178663 3.143675 0.9425832
## Interact.High.cor.Y.lm 0.9434179629 3.055823 0.9427176
## Low.cor.X.lm 0.9412438212 3.113979 0.9429167
## All.X.lm 0.9382504560 3.192316 0.9432758
## All.X.glm 0.9382504560 3.192316 NA
## All.X.bayesglm 0.9382504551 3.192316 NA
## All.X.no.rnorm.rpart 0.8688472184 4.652407 NA
## All.X.no.rnorm.rf 0.9294242789 3.412877 NA
## PTS.only.lm 0.9417946385 3.099349 0.9422064
## PTS.interact.lm 0.9421885606 3.088843 0.9422414
## max.Rsquared.fit min.RMSESD.fit
## MFO.lm NA NA
## Max.cor.Y.cv.0.rpart NA NA
## Max.cor.Y.cv.0.cp.0.rpart NA NA
## Max.cor.Y.rpart 0.7716525 1.5711267
## Max.cor.Y.lm 0.9432758 0.1709692
## Interact.High.cor.Y.lm 0.9425587 0.1530238
## Low.cor.X.lm 0.9414636 0.2222216
## All.X.lm 0.9415448 0.2207614
## All.X.glm 0.9415448 0.2207614
## All.X.bayesglm 0.9415448 0.2207615
## All.X.no.rnorm.rpart 0.7716525 1.5711267
## All.X.no.rnorm.rf 0.9398484 0.1565449
## PTS.only.lm 0.9429442 0.1709393
## PTS.interact.lm 0.9425690 0.1746107
## max.RsquaredSD.fit min.aic.fit
## MFO.lm NA NA
## Max.cor.Y.cv.0.rpart NA NA
## Max.cor.Y.cv.0.cp.0.rpart NA NA
## Max.cor.Y.rpart 0.119623950 NA
## Max.cor.Y.lm 0.005666001 NA
## Interact.High.cor.Y.lm 0.004914910 NA
## Low.cor.X.lm 0.007492174 NA
## All.X.lm 0.007401005 NA
## All.X.glm 0.007401005 4241.228
## All.X.bayesglm 0.007401005 4249.228
## All.X.no.rnorm.rpart 0.119623950 NA
## All.X.no.rnorm.rf 0.005243458 NA
## PTS.only.lm 0.005805662 NA
## PTS.interact.lm 0.005991684 NArm(ret_lst)
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end",
major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 6 fit.models_1_rf 6 0 62.684 86.424 23.74
## 7 fit.models_1_end 7 0 86.425 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 11 fit.models 7 1 43.977 86.431 42.454
## 12 fit.models 7 2 86.431 NA NAif (!is.null(glb_model_metric_smmry)) {
stats_df <- glb_models_df[, "model_id", FALSE]
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_fitobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "fit",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_OOBobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "OOB",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
print("Merging following data into glb_models_df:")
print(stats_mrg_df <- stats_df[, c(1, grep(glb_model_metric, names(stats_df)))])
print(tmp_models_df <- orderBy(~model_id, glb_models_df[, c("model_id",
grep(glb_model_metric, names(stats_df), value=TRUE))]))
tmp2_models_df <- glb_models_df[, c("model_id", setdiff(names(glb_models_df),
grep(glb_model_metric, names(stats_df), value=TRUE)))]
tmp3_models_df <- merge(tmp2_models_df, stats_mrg_df, all.x=TRUE, sort=FALSE)
print(tmp3_models_df)
print(names(tmp3_models_df))
print(glb_models_df <- subset(tmp3_models_df, select=-model_id.1))
}
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## model_id model_method
## MFO.lm MFO.lm lm
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart Max.cor.Y.rpart rpart
## Max.cor.Y.lm Max.cor.Y.lm lm
## Interact.High.cor.Y.lm Interact.High.cor.Y.lm lm
## Low.cor.X.lm Low.cor.X.lm lm
## All.X.lm All.X.lm lm
## All.X.glm All.X.glm glm
## All.X.bayesglm All.X.bayesglm bayesglm
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart rpart
## All.X.no.rnorm.rf All.X.no.rnorm.rf rf
## PTS.only.lm PTS.only.lm lm
## PTS.interact.lm PTS.interact.lm lm
## feats
## MFO.lm .rnorm
## Max.cor.Y.cv.0.rpart PTS.diff, DRB
## Max.cor.Y.cv.0.cp.0.rpart PTS.diff, DRB
## Max.cor.Y.rpart PTS.diff, DRB
## Max.cor.Y.lm PTS.diff, DRB
## Interact.High.cor.Y.lm PTS.diff, DRB, PTS.diff:oppPTS, PTS.diff:PTS, PTS.diff:FT, PTS.diff:X3P, PTS.diff:X2PA
## Low.cor.X.lm PTS.diff, DRB, AST, FT, BLK, X3P, STL, .rnorm, SeasonEnd, ORB, TOV, oppPTS
## All.X.lm PTS.diff, DRB, AST, PTS, FT, BLK, FG, FTA, X3P, STL, X3PA, X2P, .rnorm, SeasonEnd, FGA, X2PA, ORB, TOV, oppPTS
## All.X.glm PTS.diff, DRB, AST, PTS, FT, BLK, FG, FTA, X3P, STL, X3PA, X2P, .rnorm, SeasonEnd, FGA, X2PA, ORB, TOV, oppPTS
## All.X.bayesglm PTS.diff, DRB, AST, PTS, FT, BLK, FG, FTA, X3P, STL, X3PA, X2P, .rnorm, SeasonEnd, FGA, X2PA, ORB, TOV, oppPTS
## All.X.no.rnorm.rpart PTS.diff, DRB, AST, PTS, FT, BLK, FG, FTA, X3P, STL, X3PA, X2P, SeasonEnd, FGA, X2PA, ORB, TOV, oppPTS
## All.X.no.rnorm.rf PTS.diff, DRB, AST, PTS, FT, BLK, FG, FTA, X3P, STL, X3PA, X2P, SeasonEnd, FGA, X2PA, ORB, TOV, oppPTS
## PTS.only.lm PTS, oppPTS
## PTS.interact.lm oppPTS, oppPTS*PTS
## max.nTuningRuns max.R.sq.fit max.R.sq.OOB
## MFO.lm 0 3.833404e-05 0.0002542561
## Max.cor.Y.cv.0.rpart 0 0.000000e+00 0.0000000000
## Max.cor.Y.cv.0.cp.0.rpart 0 9.612337e-01 0.9260059366
## Max.cor.Y.rpart 3 7.697140e-01 0.8688472184
## Max.cor.Y.lm 1 9.427209e-01 0.9401178663
## Interact.High.cor.Y.lm 1 9.431984e-01 0.9434179629
## Low.cor.X.lm 1 9.437381e-01 0.9412438212
## All.X.lm 1 9.442961e-01 0.9382504560
## All.X.glm 1 9.442961e-01 0.9382504560
## All.X.bayesglm 1 9.442961e-01 0.9382504551
## All.X.no.rnorm.rpart 3 7.697140e-01 0.8688472184
## All.X.no.rnorm.rf 3 9.899911e-01 0.9294242789
## PTS.only.lm 1 9.423450e-01 0.9417946385
## PTS.interact.lm 1 9.424492e-01 0.9421885606
## max.Adj.R.sq.fit max.Rsquared.fit
## MFO.lm -0.0011621 NA
## Max.cor.Y.cv.0.rpart NA NA
## Max.cor.Y.cv.0.cp.0.rpart NA NA
## Max.cor.Y.rpart NA 0.7716525
## Max.cor.Y.lm 0.9425832 0.9432758
## Interact.High.cor.Y.lm 0.9427176 0.9425587
## Low.cor.X.lm 0.9429167 0.9414636
## All.X.lm 0.9432758 0.9415448
## All.X.glm NA 0.9415448
## All.X.bayesglm NA 0.9415448
## All.X.no.rnorm.rpart NA 0.7716525
## All.X.no.rnorm.rf NA 0.9398484
## PTS.only.lm 0.9422064 0.9429442
## PTS.interact.lm 0.9422414 0.9425690
## inv.elapsedtime.everything inv.elapsedtime.final
## MFO.lm 2.22222222 333.3333333
## Max.cor.Y.cv.0.rpart 1.65837479 52.6315789
## Max.cor.Y.cv.0.cp.0.rpart 2.13219616 55.5555556
## Max.cor.Y.rpart 0.96899225 55.5555556
## Max.cor.Y.lm 1.07296137 333.3333333
## Interact.High.cor.Y.lm 1.12866817 200.0000000
## Low.cor.X.lm 1.09289617 166.6666667
## All.X.lm 1.08813928 111.1111111
## All.X.glm 1.08225108 16.9491525
## All.X.bayesglm 0.21944262 8.8495575
## All.X.no.rnorm.rpart 0.86430424 15.8730159
## All.X.no.rnorm.rf 0.05948132 0.1576044
## PTS.only.lm 1.16550117 333.3333333
## PTS.interact.lm 1.17508813 333.3333333
## inv.RMSE.fit inv.RMSE.OOB inv.aic.fit
## MFO.lm 0.07853642 0.07785137 NA
## Max.cor.Y.cv.0.rpart 0.07853491 0.07784147 NA
## Max.cor.Y.cv.0.cp.0.rpart 0.39887403 0.28616242 NA
## Max.cor.Y.rpart 0.16749541 0.21494249 NA
## Max.cor.Y.lm 0.32602380 0.31809907 NA
## Interact.High.cor.Y.lm 0.32427033 0.32724404 NA
## Low.cor.X.lm 0.32061384 0.32113250 NA
## All.X.lm 0.32093590 0.31325222 NA
## All.X.glm 0.32093590 0.31325222 0.0002357808
## All.X.bayesglm 0.32093591 0.31325222 0.0002353368
## All.X.no.rnorm.rpart 0.16749541 0.21494249 NA
## All.X.no.rnorm.rf 0.31906005 0.29300790 NA
## PTS.only.lm 0.32513049 0.32264842 NA
## PTS.interact.lm 0.32405557 0.32374580 NAprint(myplot_radar(radar_inp_df=plt_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 14. Consider specifying shapes manually. if you must have them.## Warning in loop_apply(n, do.ply): Removed 7 rows containing missing values
## (geom_path).## Warning in loop_apply(n, do.ply): Removed 88 rows containing missing values
## (geom_point).## Warning in loop_apply(n, do.ply): Removed 22 rows containing missing values
## (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 14. Consider specifying shapes manually. if you must have them.
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(model_id %in% grep("random|MFO", plt_models_df$model_id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## [1] "var:min.RMSESD.fit"
## [1] "var:max.RsquaredSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "model_id", FALSE]
pltCI_models_df <- glb_models_df[, "model_id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="model_id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="model_id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("model_id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("model_id", "model_method")],
all.x=TRUE)
png(paste0(glb_out_pfx, "models_bar.png"), width=480*3, height=480*2)
print(gp <- myplot_bar(mltd_models_df, "model_id", "value", colorcol_name="model_method") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=model_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))## Warning in loop_apply(n, do.ply): Removed 3 rows containing missing values
## (position_stack).dev.off()## quartz_off_screen
## 2print(gp)## Warning in loop_apply(n, do.ply): Removed 3 rows containing missing values
## (position_stack).
#stop(here")
# used for console inspection
model_evl_terms <- c(NULL)
for (metric in glb_model_evl_criteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse=" "))
dsp_models_cols <- c("model_id", glb_model_evl_criteria)
if (glb_is_classification && glb_is_binomial)
dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, dsp_models_cols])## model_id min.RMSE.OOB max.R.sq.OOB max.Adj.R.sq.fit
## 6 Interact.High.cor.Y.lm 3.055823 0.9434179629 0.9427176
## 14 PTS.interact.lm 3.088843 0.9421885606 0.9422414
## 13 PTS.only.lm 3.099349 0.9417946385 0.9422064
## 7 Low.cor.X.lm 3.113979 0.9412438212 0.9429167
## 5 Max.cor.Y.lm 3.143675 0.9401178663 0.9425832
## 10 All.X.bayesglm 3.192316 0.9382504551 NA
## 8 All.X.lm 3.192316 0.9382504560 0.9432758
## 9 All.X.glm 3.192316 0.9382504560 NA
## 12 All.X.no.rnorm.rf 3.412877 0.9294242789 NA
## 3 Max.cor.Y.cv.0.cp.0.rpart 3.494519 0.9260059366 NA
## 4 Max.cor.Y.rpart 4.652407 0.8688472184 NA
## 11 All.X.no.rnorm.rpart 4.652407 0.8688472184 NA
## 1 MFO.lm 12.844989 0.0002542561 -0.0011621
## 2 Max.cor.Y.cv.0.rpart 12.846623 0.0000000000 NAprint(myplot_radar(radar_inp_df=dsp_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 14. Consider specifying shapes manually. if you must have them.## Warning in loop_apply(n, do.ply): Removed 6 rows containing missing values
## (geom_path).## Warning in loop_apply(n, do.ply): Removed 33 rows containing missing values
## (geom_point).## Warning in loop_apply(n, do.ply): Removed 7 rows containing missing values
## (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 14. Consider specifying shapes manually. if you must have them.
print("Metrics used for model selection:"); print(model_sel_frmla)## [1] "Metrics used for model selection:"## ~+min.RMSE.OOB - max.R.sq.OOB - max.Adj.R.sq.fitprint(sprintf("Best model id: %s", dsp_models_df[1, "model_id"]))## [1] "Best model id: Interact.High.cor.Y.lm"if (is.null(glb_sel_mdl_id)) {
glb_sel_mdl_id <- dsp_models_df[1, "model_id"]
if (glb_sel_mdl_id == "Interact.High.cor.Y.glm") {
warning("glb_sel_mdl_id: Interact.High.cor.Y.glm; myextract_mdl_feats does not currently support interaction terms")
glb_sel_mdl_id <- dsp_models_df[2, "model_id"]
}
} else
print(sprintf("User specified selection: %s", glb_sel_mdl_id))
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.3505 -2.0452 -0.1462 2.0161 9.1299
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.608e+01 2.228e+00 16.190 < 2e-16 ***
## PTS.diff 4.806e-02 6.703e-03 7.169 1.67e-12 ***
## DRB 2.007e-03 9.163e-04 2.190 0.0288 *
## `PTS.diff:oppPTS` -2.819e-07 8.685e-07 -0.325 0.7456
## `PTS.diff:PTS` 1.669e-06 1.180e-06 1.414 0.1578
## `PTS.diff:FT` -4.279e-06 2.589e-06 -1.653 0.0988 .
## `PTS.diff:X3P` -1.031e-05 5.895e-06 -1.748 0.0808 .
## `PTS.diff:X2PA` -2.865e-06 1.771e-06 -1.617 0.1062
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.049 on 827 degrees of freedom
## Multiple R-squared: 0.9432, Adjusted R-squared: 0.9427
## F-statistic: 1962 on 7 and 827 DF, p-value: < 2.2e-16## [1] TRUE# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
glb_get_predictions <- function(df, mdl_id, rsp_var_out, prob_threshold_def=NULL) {
mdl <- glb_models_lst[[mdl_id]]
rsp_var_out <- paste0(rsp_var_out, mdl_id)
if (glb_is_regression) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
print(myplot_scatter(df, glb_rsp_var, rsp_var_out, smooth=TRUE))
df[, paste0(rsp_var_out, ".err")] <-
abs(df[, rsp_var_out] - df[, glb_rsp_var])
print(head(orderBy(reformulate(c("-", paste0(rsp_var_out, ".err"))),
df)))
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$model_id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")[, 2]
df[, rsp_var_out] <-
factor(levels(df[, glb_rsp_var])[
(df[, paste0(rsp_var_out, ".prob")] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# prediction stats already reported by myfit_mdl ???
}
if (glb_is_classification && !glb_is_binomial) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")
}
return(df)
}
glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
rsp_var_out=glb_rsp_var_out)
## SeasonEnd Team Playoffs W PTS oppPTS FG FGA X2P
## 854 2013 Oklahoma City Thunder 1 60 8669 7914 3126 6504 2528
## 863 2013 Washington Wizards 0 29 7644 7852 2910 6693 2365
## 845 2013 Houston Rockets 1 45 8688 8403 3124 6782 2257
## 840 2013 Cleveland Cavaliers 0 24 7913 8297 2993 6901 2446
## 838 2013 Charlotte Bobcats 0 21 7661 8418 2823 6649 2354
## 848 2013 Memphis Grizzlies 1 56 7659 7319 2964 6679 2582
## X2PA X3P X3PA FT FTA ORB DRB AST STL BLK TOV .rownames .src
## 854 4916 598 1588 1819 2196 854 2725 1753 679 624 1253 19 Test
## 863 5198 545 1495 1279 1746 887 2652 1775 598 376 1238 28 Test
## 845 4413 867 2369 1573 2087 909 2652 1902 679 359 1348 10 Test
## 840 5320 547 1581 1380 1826 1004 2359 1694 647 334 1149 5 Test
## 838 5250 469 1399 1546 2060 917 2389 1587 591 479 1153 3 Test
## 848 5572 382 1107 1349 1746 1059 2445 1715 703 436 1144 13 Test
## .rnorm PTS.diff Team.fctr
## 854 1.59350961 755 Oklahoma City Thunder
## 863 1.43540954 -208 Washington Wizards
## 845 -0.27976609 285 Houston Rockets
## 840 -0.02319101 -384 Cleveland Cavaliers
## 838 0.34644930 -757 Charlotte Bobcats
## 848 0.67432067 340 Memphis Grizzlies
## W.predict.Interact.High.cor.Y.lm W.predict.Interact.High.cor.Y.lm.err
## 854 65.90764 5.907643
## 863 34.61608 5.616085
## 845 50.48699 5.486987
## 840 28.47063 4.470631
## 838 16.66341 4.336589
## 848 52.24151 3.758489predct_accurate_var_name <- paste0(glb_rsp_var_out, glb_sel_mdl_id, ".accurate")
glb_OOBobs_df[, predct_accurate_var_name] <-
(glb_OOBobs_df[, glb_rsp_var] ==
glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)])
#stop(here"); #sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df
glb_featsimp_df <-
myget_feats_importance(mdl=glb_sel_mdl, featsimp_df=NULL)
glb_featsimp_df[, paste0(glb_sel_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## importance Interact.High.cor.Y.lm.importance
## PTS.diff 100.00000 100.00000
## DRB 27.25871 27.25871
## `PTS.diff:X3P` 20.79874 20.79874
## `PTS.diff:FT` 19.40171 19.40171
## `PTS.diff:X2PA` 18.88785 18.88785
## `PTS.diff:PTS` 15.91326 15.91326
## `PTS.diff:oppPTS` 0.00000 0.00000# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
featsimp_df <- glb_featsimp_df
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <- ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -importance.max, summaryBy(importance ~ feat + feat.interact,
data=featsimp_df, FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ", nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- paste0(glb_rsp_var_out, mdl_id)
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars=var,
measure.vars=c(glb_rsp_var, rsp_var_out))
# if (var == "<feat_name>") print(myplot_scatter(plot_df, var, "value",
# facet_colcol_name="variable") +
# geom_vline(xintercept=<divider_val>, linetype="dotted")) else
print(myplot_scatter(plot_df, var, "value", colorcol_name="variable",
facet_colcol_name="variable", jitter=TRUE) +
guides(color=FALSE))
}
if (glb_is_regression) {
if (nrow(featsimp_df) == 0)
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glb_id_var)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (nrow(featsimp_df) == 0)
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var,
rsp_var_out=rsp_var_out,
id_vars=glb_id_var,
prob_threshold=prob_threshold)
# + geom_hline(yintercept=<divider_val>, linetype = "dotted")
)
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_OOBobs_df, mdl_id =
## glb_sel_mdl_id): Limiting important feature scatter plots to 5 out of 7
## SeasonEnd Team Playoffs W PTS oppPTS FG FGA X2P
## 854 2013 Oklahoma City Thunder 1 60 8669 7914 3126 6504 2528
## 863 2013 Washington Wizards 0 29 7644 7852 2910 6693 2365
## 845 2013 Houston Rockets 1 45 8688 8403 3124 6782 2257
## 840 2013 Cleveland Cavaliers 0 24 7913 8297 2993 6901 2446
## 838 2013 Charlotte Bobcats 0 21 7661 8418 2823 6649 2354
## X2PA X3P X3PA FT FTA ORB DRB AST STL BLK TOV .rownames .src
## 854 4916 598 1588 1819 2196 854 2725 1753 679 624 1253 19 Test
## 863 5198 545 1495 1279 1746 887 2652 1775 598 376 1238 28 Test
## 845 4413 867 2369 1573 2087 909 2652 1902 679 359 1348 10 Test
## 840 5320 547 1581 1380 1826 1004 2359 1694 647 334 1149 5 Test
## 838 5250 469 1399 1546 2060 917 2389 1587 591 479 1153 3 Test
## .rnorm PTS.diff Team.fctr
## 854 1.59350961 755 Oklahoma City Thunder
## 863 1.43540954 -208 Washington Wizards
## 845 -0.27976609 285 Houston Rockets
## 840 -0.02319101 -384 Cleveland Cavaliers
## 838 0.34644930 -757 Charlotte Bobcats
## W.predict.Interact.High.cor.Y.lm W.predict.Interact.High.cor.Y.lm.err
## 854 65.90764 5.907643
## 863 34.61608 5.616085
## 845 50.48699 5.486987
## 840 28.47063 4.470631
## 838 16.66341 4.336589
## W.predict.Interact.High.cor.Y.lm.accurate .label
## 854 FALSE 19
## 863 FALSE 28
## 845 FALSE 10
## 840 FALSE 5
## 838 FALSE 3
# gather predictions from models better than MFO.*
#mdl_id <- "Conditional.X.rf"
#mdl_id <- "Conditional.X.cp.0.rpart"
#mdl_id <- "Conditional.X.rpart"
# glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id,
# glb_rsp_var_out)
# print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, mdl_id)],
# glb_OOBobs_df[, glb_rsp_var])$table))
# FN_OOB_ids <- c(4721, 4020, 693, 92)
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_feats_df$id[1:5]])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
write.csv(glb_OOBobs_df[, c(glb_id_var,
grep(glb_rsp_var, names(glb_OOBobs_df), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_sel_mdl_id), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# dsp_tbl(Headline.contains="[Ee]bola")
# sum(sel_obs(Headline.contains="[Ee]bola"))
# ftable(xtabs(Popular ~ NewsDesk.fctr, data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,]))
# xtabs(NewsDesk ~ Popular, #Popular ~ NewsDesk.fctr,
# data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# exclude=NULL)
# print(mycreate_xtab_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# tbl_col_names=c("Popular", "NewsDesk")))
# write.csv(glb_chunks_df, paste0(glb_out_pfx, tail(glb_chunks_df, 1)$label, "_",
# tail(glb_chunks_df, 1)$step_minor, "_chunks1.csv"),
# row.names=FALSE)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 12 fit.models 7 2 86.431 99.704 13.273
## 13 fit.models 7 3 99.704 NA NAprint(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## [1] "W.predict.Interact.High.cor.Y.lm"
## [2] "W.predict.Interact.High.cor.Y.lm.err"
## [3] "W.predict.Interact.High.cor.Y.lm.accurate"for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_sel_mdl, glb_sel_mdl_id,
glb_model_type,
file=paste0(glb_out_pfx, "selmdl_dsk.RData"))
#load(paste0(glb_out_pfx, "selmdl_dsk.RData"))
rm(ret_lst)## Warning in rm(ret_lst): object 'ret_lst' not foundreplay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 13 fit.models 7 3 99.704 103.77 4.067
## 14 fit.data.training 8 0 103.771 NA NAStep 8.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
# To create specific models
# glb_fin_mdl_id <- NULL; glb_fin_mdl <- NULL;
# glb_sel_mdl_id <- "Conditional.X.cp.0.rpart";
# glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]]; print(glb_sel_mdl)
if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_sel_mdl
} else {
# print(mdl_feats_df <- myextract_mdl_feats(sel_mdl=glb_sel_mdl,
# entity_df=glb_fitobs_df))
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the model_id
model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
tune_finmdl_df <- NULL
if (nrow(glb_sel_mdl$bestTune) > 0) {
for (param in names(glb_sel_mdl$bestTune)) {
#print(sprintf("param: %s", param))
if (glb_sel_mdl$bestTune[1, param] != "none")
tune_finmdl_df <- rbind(tune_finmdl_df,
data.frame(parameter=param,
min=glb_sel_mdl$bestTune[1, param],
max=glb_sel_mdl$bestTune[1, param],
by=1)) # by val does not matter
}
}
# Sync with parameters in mydsutils.R
require(gdata)
ret_lst <- myfit_mdl(model_id="Final", model_method=model_method,
indep_vars_vctr=trim(unlist(strsplit(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"feats"], "[,]"))),
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnobs_df, OOB_df=NULL,
n_cv_folds=glb_n_cv_folds, tune_models_df=tune_finmdl_df,
# Automate from here
# Issues if glb_sel_mdl$method == "rf" b/c trainControl is "oob"; not "cv"
model_loss_mtrx=glb_model_metric_terms,
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize)
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "model_id"]
}## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
##
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
##
## Attaching package: 'gdata'
##
## The following object is masked from 'package:randomForest':
##
## combine
##
## The following objects are masked from 'package:dplyr':
##
## combine, first, last
##
## The following object is masked from 'package:stats':
##
## nobs
##
## The following object is masked from 'package:utils':
##
## object.size## [1] "fitting model: Final.lm"
## [1] " indep_vars: PTS.diff, DRB, PTS.diff:oppPTS, PTS.diff:PTS, PTS.diff:FT, PTS.diff:X3P, PTS.diff:X2PA"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.3505 -2.0452 -0.1462 2.0161 9.1299
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.608e+01 2.228e+00 16.190 < 2e-16 ***
## PTS.diff 4.806e-02 6.703e-03 7.169 1.67e-12 ***
## DRB 2.007e-03 9.163e-04 2.190 0.0288 *
## `PTS.diff:oppPTS` -2.819e-07 8.685e-07 -0.325 0.7456
## `PTS.diff:PTS` 1.669e-06 1.180e-06 1.414 0.1578
## `PTS.diff:FT` -4.279e-06 2.589e-06 -1.653 0.0988 .
## `PTS.diff:X3P` -1.031e-05 5.895e-06 -1.748 0.0808 .
## `PTS.diff:X2PA` -2.865e-06 1.771e-06 -1.617 0.1062
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.049 on 827 degrees of freedom
## Multiple R-squared: 0.9432, Adjusted R-squared: 0.9427
## F-statistic: 1962 on 7 and 827 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"
## model_id model_method
## 1 Final.lm lm
## feats
## 1 PTS.diff, DRB, PTS.diff:oppPTS, PTS.diff:PTS, PTS.diff:FT, PTS.diff:X3P, PTS.diff:X2PA
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 0.892 0.005
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.Rsquared.fit
## 1 0.9431984 3.083847 0.9427176 0.9425587
## min.RMSESD.fit max.RsquaredSD.fit
## 1 0.1530238 0.00491491rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 14 fit.data.training 8 0 103.771 107.737 3.967
## 15 fit.data.training 8 1 107.738 NA NAglb_trnobs_df <- glb_get_predictions(df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "opt.prob.threshold.OOB"], NULL))
## SeasonEnd Team Playoffs W PTS oppPTS FG FGA X2P
## 158 1986 Seattle SuperSonics 0 31 8564 8572 3335 7059 3256
## 318 1993 Dallas Mavericks 0 11 8141 9387 3164 7271 2881
## 148 1986 Los Angeles Clippers 0 32 8907 9475 3388 7165 3324
## 425 1997 Charlotte Hornets 1 54 8108 7955 2988 6342 2397
## 387 1995 Phoenix Suns 1 59 9073 8755 3356 6967 2772
## 459 1998 Detroit Pistons 0 37 7721 7592 2862 6373 2569
## X2PA X3P X3PA FT FTA ORB DRB AST STL BLK TOV .rownames .src
## 158 6759 79 300 1815 2331 1145 2256 1977 745 295 1435 158 Train
## 318 6434 283 837 1530 2171 1234 2265 1683 649 355 1459 318 Train
## 148 6936 64 229 2067 2683 1159 2258 1968 694 501 1506 148 Train
## 425 4960 591 1382 1541 1984 910 2298 2021 597 349 1203 425 Train
## 387 5383 584 1584 1777 2352 1027 2403 2198 687 312 1167 387 Train
## 459 5435 293 938 1704 2288 1044 2338 1597 678 344 1198 459 Train
## .rnorm PTS.diff Team.fctr W.predict.Final.lm
## 158 0.8923638 -8 Seattle SuperSonics 40.350529
## 318 0.7199727 -1246 Dallas Mavericks 1.870137
## 148 0.8696714 -568 Los Angeles Clippers 23.071963
## 425 -0.4494398 153 Charlotte Hornets 45.656013
## 387 -0.3689781 318 Phoenix Suns 50.978312
## 459 0.8742224 129 Detroit Pistons 45.017996
## W.predict.Final.lm.err
## 158 9.350529
## 318 9.129863
## 148 8.928037
## 425 8.343987
## 387 8.021688
## 459 8.017996sav_featsimp_df <- glb_featsimp_df
#glb_feats_df <- sav_feats_df
# glb_feats_df <- mymerge_feats_importance(feats_df=glb_feats_df, sel_mdl=glb_fin_mdl,
# entity_df=glb_trnobs_df)
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl, featsimp_df=glb_featsimp_df)
glb_featsimp_df[, paste0(glb_fin_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## Interact.High.cor.Y.lm.importance importance
## PTS.diff 100.00000 100.00000
## DRB 27.25871 27.25871
## `PTS.diff:X3P` 20.79874 20.79874
## `PTS.diff:FT` 19.40171 19.40171
## `PTS.diff:X2PA` 18.88785 18.88785
## `PTS.diff:PTS` 15.91326 15.91326
## `PTS.diff:oppPTS` 0.00000 0.00000
## Final.lm.importance
## PTS.diff 100.00000
## DRB 27.25871
## `PTS.diff:X3P` 20.79874
## `PTS.diff:FT` 19.40171
## `PTS.diff:X2PA` 18.88785
## `PTS.diff:PTS` 15.91326
## `PTS.diff:oppPTS` 0.00000if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id): Limiting important feature scatter plots to 5 out of 7
## SeasonEnd Team Playoffs W PTS oppPTS FG FGA X2P
## 158 1986 Seattle SuperSonics 0 31 8564 8572 3335 7059 3256
## 318 1993 Dallas Mavericks 0 11 8141 9387 3164 7271 2881
## 148 1986 Los Angeles Clippers 0 32 8907 9475 3388 7165 3324
## 425 1997 Charlotte Hornets 1 54 8108 7955 2988 6342 2397
## 387 1995 Phoenix Suns 1 59 9073 8755 3356 6967 2772
## X2PA X3P X3PA FT FTA ORB DRB AST STL BLK TOV .rownames .src
## 158 6759 79 300 1815 2331 1145 2256 1977 745 295 1435 158 Train
## 318 6434 283 837 1530 2171 1234 2265 1683 649 355 1459 318 Train
## 148 6936 64 229 2067 2683 1159 2258 1968 694 501 1506 148 Train
## 425 4960 591 1382 1541 1984 910 2298 2021 597 349 1203 425 Train
## 387 5383 584 1584 1777 2352 1027 2403 2198 687 312 1167 387 Train
## .rnorm PTS.diff Team.fctr W.predict.Final.lm
## 158 0.8923638 -8 Seattle SuperSonics 40.350529
## 318 0.7199727 -1246 Dallas Mavericks 1.870137
## 148 0.8696714 -568 Los Angeles Clippers 23.071963
## 425 -0.4494398 153 Charlotte Hornets 45.656013
## 387 -0.3689781 318 Phoenix Suns 50.978312
## W.predict.Final.lm.err .label
## 158 9.350529 158
## 318 9.129863 318
## 148 8.928037 148
## 425 8.343987 425
## 387 8.021688 387
dsp_feats_vctr <- c(NULL)
for(var in grep(".importance", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glb_trnobs_df[glb_trnobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_trnobs_df), value=TRUE)])
print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## [1] "W.predict.Final.lm" "W.predict.Final.lm.err"for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## character(0)for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 15 fit.data.training 8 1 107.738 112.791 5.053
## 16 predict.data.new 9 0 112.791 NA NAStep 9.0: predict data new
# Compute final model predictions
glb_newobs_df <- glb_get_predictions(glb_newobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"], NULL))
## SeasonEnd Team Playoffs W PTS oppPTS FG FGA X2P
## 854 2013 Oklahoma City Thunder 1 60 8669 7914 3126 6504 2528
## 863 2013 Washington Wizards 0 29 7644 7852 2910 6693 2365
## 845 2013 Houston Rockets 1 45 8688 8403 3124 6782 2257
## 840 2013 Cleveland Cavaliers 0 24 7913 8297 2993 6901 2446
## 838 2013 Charlotte Bobcats 0 21 7661 8418 2823 6649 2354
## 848 2013 Memphis Grizzlies 1 56 7659 7319 2964 6679 2582
## X2PA X3P X3PA FT FTA ORB DRB AST STL BLK TOV .rownames .src
## 854 4916 598 1588 1819 2196 854 2725 1753 679 624 1253 19 Test
## 863 5198 545 1495 1279 1746 887 2652 1775 598 376 1238 28 Test
## 845 4413 867 2369 1573 2087 909 2652 1902 679 359 1348 10 Test
## 840 5320 547 1581 1380 1826 1004 2359 1694 647 334 1149 5 Test
## 838 5250 469 1399 1546 2060 917 2389 1587 591 479 1153 3 Test
## 848 5572 382 1107 1349 1746 1059 2445 1715 703 436 1144 13 Test
## .rnorm PTS.diff Team.fctr W.predict.Final.lm
## 854 1.59350961 755 Oklahoma City Thunder 65.90764
## 863 1.43540954 -208 Washington Wizards 34.61608
## 845 -0.27976609 285 Houston Rockets 50.48699
## 840 -0.02319101 -384 Cleveland Cavaliers 28.47063
## 838 0.34644930 -757 Charlotte Bobcats 16.66341
## 848 0.67432067 340 Memphis Grizzlies 52.24151
## W.predict.Final.lm.err
## 854 5.907643
## 863 5.616085
## 845 5.486987
## 840 4.470631
## 838 4.336589
## 848 3.758489if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_newobs_df, mdl_id =
## glb_fin_mdl_id): Limiting important feature scatter plots to 5 out of 7
## SeasonEnd Team Playoffs W PTS oppPTS FG FGA X2P
## 854 2013 Oklahoma City Thunder 1 60 8669 7914 3126 6504 2528
## 863 2013 Washington Wizards 0 29 7644 7852 2910 6693 2365
## 845 2013 Houston Rockets 1 45 8688 8403 3124 6782 2257
## 840 2013 Cleveland Cavaliers 0 24 7913 8297 2993 6901 2446
## 838 2013 Charlotte Bobcats 0 21 7661 8418 2823 6649 2354
## X2PA X3P X3PA FT FTA ORB DRB AST STL BLK TOV .rownames .src
## 854 4916 598 1588 1819 2196 854 2725 1753 679 624 1253 19 Test
## 863 5198 545 1495 1279 1746 887 2652 1775 598 376 1238 28 Test
## 845 4413 867 2369 1573 2087 909 2652 1902 679 359 1348 10 Test
## 840 5320 547 1581 1380 1826 1004 2359 1694 647 334 1149 5 Test
## 838 5250 469 1399 1546 2060 917 2389 1587 591 479 1153 3 Test
## .rnorm PTS.diff Team.fctr W.predict.Final.lm
## 854 1.59350961 755 Oklahoma City Thunder 65.90764
## 863 1.43540954 -208 Washington Wizards 34.61608
## 845 -0.27976609 285 Houston Rockets 50.48699
## 840 -0.02319101 -384 Cleveland Cavaliers 28.47063
## 838 0.34644930 -757 Charlotte Bobcats 16.66341
## W.predict.Final.lm.err .label
## 854 5.907643 19
## 863 5.616085 28
## 845 5.486987 10
## 840 4.470631 5
## 838 4.336589 3
if (glb_is_classification && glb_is_binomial) {
submit_df <- glb_newobs_df[, c(glb_id_var,
paste0(glb_rsp_var_out, glb_fin_mdl_id, ".prob"))]
names(submit_df)[2] <- "Probability1"
} else submit_df <- glb_newobs_df[, c(glb_id_var,
paste0(glb_rsp_var_out, glb_fin_mdl_id))]
write.csv(submit_df,
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_fin_mdl_id), fixed=TRUE),
"_submit.csv"), row.names=FALSE)
# print(orderBy(~ -max.auc.OOB, glb_models_df[, c("model_id",
# "max.auc.OOB", "max.Accuracy.OOB")]))
if (glb_is_classification && glb_is_binomial)
print(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"])
print(sprintf("glb_sel_mdl_id: %s", glb_sel_mdl_id))## [1] "glb_sel_mdl_id: Interact.High.cor.Y.lm"print(sprintf("glb_fin_mdl_id: %s", glb_fin_mdl_id))## [1] "glb_fin_mdl_id: Final.lm"print(dim(glb_fitobs_df))## [1] 835 25print(dsp_models_df)## model_id min.RMSE.OOB max.R.sq.OOB max.Adj.R.sq.fit
## 6 Interact.High.cor.Y.lm 3.055823 0.9434179629 0.9427176
## 14 PTS.interact.lm 3.088843 0.9421885606 0.9422414
## 13 PTS.only.lm 3.099349 0.9417946385 0.9422064
## 7 Low.cor.X.lm 3.113979 0.9412438212 0.9429167
## 5 Max.cor.Y.lm 3.143675 0.9401178663 0.9425832
## 10 All.X.bayesglm 3.192316 0.9382504551 NA
## 8 All.X.lm 3.192316 0.9382504560 0.9432758
## 9 All.X.glm 3.192316 0.9382504560 NA
## 12 All.X.no.rnorm.rf 3.412877 0.9294242789 NA
## 3 Max.cor.Y.cv.0.cp.0.rpart 3.494519 0.9260059366 NA
## 4 Max.cor.Y.rpart 4.652407 0.8688472184 NA
## 11 All.X.no.rnorm.rpart 4.652407 0.8688472184 NA
## 1 MFO.lm 12.844989 0.0002542561 -0.0011621
## 2 Max.cor.Y.cv.0.rpart 12.846623 0.0000000000 NAif (glb_is_regression) {
print(sprintf("%s OOB RMSE: %0.4f", glb_sel_mdl_id,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "min.RMSE.OOB"]))
if (!is.null(glb_category_vars)) {
stop("not implemented yet")
tmp_OOBobs_df <- glb_OOBobs_df[, c(glb_category_vars, predct_accurate_var_name)]
names(tmp_OOBobs_df)[length(names(tmp_OOBobs_df))] <- "accurate.OOB"
aOOB_ctgry_df <- mycreate_xtab_df(tmp_OOBobs_df, names(tmp_OOBobs_df))
aOOB_ctgry_df[is.na(aOOB_ctgry_df)] <- 0
aOOB_ctgry_df <- mutate(aOOB_ctgry_df,
.n.OOB = accurate.OOB.FALSE + accurate.OOB.TRUE,
max.accuracy.OOB = accurate.OOB.TRUE / .n.OOB)
#intersect(names(glb_ctgry_df), names(aOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, aOOB_ctgry_df, all=TRUE)
print(orderBy(~-accurate.OOB.FALSE, glb_ctgry_df))
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
pred_stats_df <-
mypredict_mdl(mdl=glb_models_lst[[glb_fin_mdl_id]],
df=glb_newobs_df,
rsp_var=glb_rsp_var,
rsp_var_out=glb_rsp_var_out,
model_id_method=glb_fin_mdl_id,
label="new",
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize,
ret_type="stats")
print(sprintf("%s prediction stats for glb_newobs_df:", glb_fin_mdl_id))
print(pred_stats_df)
}
} ## [1] "Interact.High.cor.Y.lm OOB RMSE: 3.0558"
## [1] "Final.lm prediction stats for glb_newobs_df:"
## model_id max.R.sq.new min.RMSE.new
## 1 Final.lm 0.943418 3.055823if (glb_is_classification) {
print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id))
print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[, glb_rsp_var])$table))
if (!is.null(glb_category_vars)) {
tmp_OOBobs_df <- glb_OOBobs_df[, c(glb_category_vars, predct_accurate_var_name)]
names(tmp_OOBobs_df)[length(names(tmp_OOBobs_df))] <- "accurate.OOB"
aOOB_ctgry_df <- mycreate_xtab_df(tmp_OOBobs_df, names(tmp_OOBobs_df))
aOOB_ctgry_df[is.na(aOOB_ctgry_df)] <- 0
aOOB_ctgry_df <- mutate(aOOB_ctgry_df,
.n.OOB = accurate.OOB.FALSE + accurate.OOB.TRUE,
max.accuracy.OOB = accurate.OOB.TRUE / .n.OOB)
#intersect(names(glb_ctgry_df), names(aOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, aOOB_ctgry_df, all=TRUE)
print(orderBy(~-accurate.OOB.FALSE, glb_ctgry_df))
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
print(sprintf("%s new confusion matrix & accuracy: ", glb_fin_mdl_id))
print(t(confusionMatrix(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)],
glb_newobs_df[, glb_rsp_var])$table))
}
}
dsp_myCategory_conf_mtrx <- function(myCategory) {
print(sprintf("%s OOB::myCategory=%s confusion matrix & accuracy: ",
glb_sel_mdl_id, myCategory))
print(t(confusionMatrix(
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, glb_rsp_var])$table))
print(sum(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
predct_accurate_var_name]) /
nrow(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, ]))
err_ids <- glb_OOBobs_df[(glb_OOBobs_df$myCategory == myCategory) &
(!glb_OOBobs_df[, predct_accurate_var_name]), glb_id_var]
OOB_FNerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 1),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FN errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FNerr_df)))
print(OOB_FNerr_df)
OOB_FPerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 0),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FP errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FPerr_df)))
print(OOB_FPerr_df)
}
#dsp_myCategory_conf_mtrx(myCategory="OpEd#Opinion#")
#dsp_myCategory_conf_mtrx(myCategory="Business#Business Day#Dealbook")
#dsp_myCategory_conf_mtrx(myCategory="##")
# if (glb_is_classification) {
# print("FN_OOB_ids:")
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# print(dsp_vctr <- colSums(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# setdiff(grep("[HSA].", names(glb_OOBobs_df), value=TRUE),
# union(myfind_chr_cols_df(glb_OOBobs_df),
# grep(".fctr", names(glb_OOBobs_df), fixed=TRUE, value=TRUE)))]))
# }
dsp_hdlpfx_results <- function(hdlpfx) {
print(hdlpfx)
print(glb_OOBobs_df[glb_OOBobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_newobs_df), value=TRUE)])
print(dsp_vctr <- colSums(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
setdiff(grep("[HSA]\\.", names(glb_newobs_df), value=TRUE),
union(myfind_chr_cols_df(glb_newobs_df),
grep(".fctr", names(glb_newobs_df), fixed=TRUE, value=TRUE)))]))
print(dsp_vctr <- dsp_vctr[dsp_vctr != 0])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
union(names(dsp_vctr), myfind_chr_cols_df(glb_newobs_df))])
}
#dsp_hdlpfx_results(hdlpfx="Ask Well::")
# print("myMisc::|OpEd|blank|blank|1:")
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% c(6446),
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# c("WordCount", "WordCount.log", "myMultimedia",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"), ],
# c(glb_rsp_var, "myMultimedia")))
# dsp_chisq.test(Headline.contains="[Vi]deo")
# print(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline")])
# print(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola", Popular=1),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.ConditionalX.y & is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
print(orderBy(as.formula(paste0("~ -", glb_sel_mdl_id, ".importance")), glb_featsimp_df))## Interact.High.cor.Y.lm.importance importance
## PTS.diff 100.00000 100.00000
## DRB 27.25871 27.25871
## `PTS.diff:X3P` 20.79874 20.79874
## `PTS.diff:FT` 19.40171 19.40171
## `PTS.diff:X2PA` 18.88785 18.88785
## `PTS.diff:PTS` 15.91326 15.91326
## `PTS.diff:oppPTS` 0.00000 0.00000
## Final.lm.importance
## PTS.diff 100.00000
## DRB 27.25871
## `PTS.diff:X3P` 20.79874
## `PTS.diff:FT` 19.40171
## `PTS.diff:X2PA` 18.88785
## `PTS.diff:PTS` 15.91326
## `PTS.diff:oppPTS` 0.00000# players_df <- data.frame(id=c("Chavez", "Giambi", "Menechino", "Myers", "Pena"),
# OBP=c(0.338, 0.391, 0.369, 0.313, 0.361),
# SLG=c(0.540, 0.450, 0.374, 0.447, 0.500),
# cost=c(1400000, 1065000, 295000, 800000, 300000))
# players_df$RS.predict <- predict(glb_models_lst[[csm_mdl_id]], players_df)
# print(orderBy(~ -RS.predict, players_df))
if (length(diff <- setdiff(names(glb_trnobs_df), names(glb_allobs_df))) > 0)
print(diff)
for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
if (length(diff <- setdiff(names(glb_fitobs_df), names(glb_allobs_df))) > 0)
print(diff)
if (length(diff <- setdiff(names(glb_OOBobs_df), names(glb_allobs_df))) > 0)
print(diff)
for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
if (length(diff <- setdiff(names(glb_newobs_df), names(glb_allobs_df))) > 0)
print(diff)
if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "prdnew_dsk.RData"))
rm(submit_df, tmp_OOBobs_df)## Warning in rm(submit_df, tmp_OOBobs_df): object 'tmp_OOBobs_df' not found# tmp_replay_lst <- replay.petrisim(pn=glb_analytics_pn,
# replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
# "data.new.prediction")), flip_coord=TRUE)
# print(ggplot.petrinet(tmp_replay_lst[["pn"]]) + coord_flip())
glb_chunks_df <- myadd_chunk(glb_chunks_df, "display.session.info", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 16 predict.data.new 9 0 112.791 117.568 4.777
## 17 display.session.info 10 0 117.568 NA NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor bgn end elapsed
## 11 fit.models 7 1 43.977 86.431 42.454
## 10 fit.models 7 0 25.441 43.976 18.535
## 12 fit.models 7 2 86.431 99.704 13.273
## 2 inspect.data 2 0 11.902 20.193 8.291
## 15 fit.data.training 8 1 107.738 112.791 5.053
## 16 predict.data.new 9 0 112.791 117.568 4.777
## 13 fit.models 7 3 99.704 103.770 4.067
## 14 fit.data.training 8 0 103.771 107.737 3.967
## 3 scrub.data 2 1 20.193 22.958 2.765
## 6 extract.features 3 0 23.073 24.182 1.109
## 8 select.features 5 0 24.451 25.144 0.693
## 1 import.data 1 0 11.436 11.902 0.466
## 9 partition.data.training 6 0 25.144 25.441 0.297
## 7 cluster.data 4 0 24.182 24.451 0.269
## 5 manage.missing.data 2 3 22.991 23.073 0.082
## 4 transform.data 2 2 22.959 22.991 0.032
## duration
## 11 42.454
## 10 18.535
## 12 13.273
## 2 8.291
## 15 5.053
## 16 4.777
## 13 4.066
## 14 3.966
## 3 2.765
## 6 1.109
## 8 0.693
## 1 0.466
## 9 0.297
## 7 0.269
## 5 0.082
## 4 0.032
## [1] "Total Elapsed Time: 117.568 secs"
## R version 3.2.0 (2015-04-16)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.3 (Yosemite)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] grid parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] gdata_2.16.1 randomForest_4.6-10 arm_1.8-5
## [4] lme4_1.1-7 Rcpp_0.11.6 Matrix_1.2-1
## [7] MASS_7.3-40 rpart.plot_1.5.2 rpart_4.1-9
## [10] reshape2_1.4.1 dplyr_0.4.1 plyr_1.8.2
## [13] doMC_1.3.3 iterators_1.0.7 foreach_1.4.2
## [16] doBy_4.5-13 survival_2.38-1 caret_6.0-47
## [19] ggplot2_1.0.1 lattice_0.20-31
##
## loaded via a namespace (and not attached):
## [1] compiler_3.2.0 RColorBrewer_1.1-2 formatR_1.2
## [4] nloptr_1.0.4 tools_3.2.0 digest_0.6.8
## [7] evaluate_0.7 nlme_3.1-120 gtable_0.1.2
## [10] mgcv_1.8-6 DBI_0.3.1 yaml_2.1.13
## [13] brglm_0.5-9 SparseM_1.6 proto_0.3-10
## [16] coda_0.17-1 BradleyTerry2_1.0-6 stringr_1.0.0
## [19] knitr_1.10.5 gtools_3.5.0 nnet_7.3-9
## [22] rmarkdown_0.6.1 minqa_1.2.4 car_2.0-25
## [25] magrittr_1.5 scales_0.2.4 codetools_0.2-11
## [28] htmltools_0.2.6 splines_3.2.0 abind_1.4-3
## [31] assertthat_0.1 pbkrtest_0.4-2 colorspace_1.2-6
## [34] labeling_0.3 quantreg_5.11 stringi_0.4-1
## [37] lazyeval_0.1.10 munsell_0.4.2